Chapter 2: System governance

To improve outcomes in cancer care and ensure innovations deliver their full potential, a strong governance model is required – underpinned by comprehensive, evidence-based, transparently monitored and well-funded national cancer control plans (NCCPs).^25,26

NCCPs are an increasingly frequent form of governance used by cancer planners,²⁷ promoted by the World Health Organization (WHO)²⁸ and the Union for International Cancer Control.²⁹ They provide a roadmap for cancer prevention, detection, diagnosis, acute and long-term treatment, support for survivors, and palliative care.³⁰ However, the quality, adherence and funding of NCCPs is highly variable between and within countries. In the EU, for example, only one third of NCCPs addressed all areas of cancer care, only 27% included financing strategies and 50% lacked reference to radiotherapy strategies, demonstrating disparities in treatment access.³¹ These gaps often result from unachievable goals, lack of robust costing and implementation frameworks, and budgets that fail to support guideline-concordant care.²⁷

By 2040, we envision cancer governance as agile, collaborative, and innovative, underpinned by digital-first, data-driven, and adaptable frameworks. NCCPs should be comprehensive, dynamic and infrastructure focused. To achieve this, we must move beyond the limitations of today’s short-term policy cycles³² to a future-focused governance model, with plans supported by horizon scanning to enable planners to keep pace with advancements in care and to ensure no patient is left behind. These NCCPs must be supported by strong health systems supported by key pillars – service delivery, health workforce, health information systems, access to essential medicines, financing and leadership/governance. These pillars were drawn from the WHO’s Health Systems Framework³³ and have been discussed in more detail below.

Fact sheet

Governance – The role of National Cancer Control Plans (NCCPs)

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Fact sheet

Governance – Implementation of NCCPs

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Data infrastructure and collection

Data collection: Effective data collection is essential to understand cancer and prognosis, understand health inequalities, evaluate current practices and develop better treatments.³⁴ Yet the rich real-world data which exists in electronic health records (EHR) is difficult to access.³⁵ LMICs in particular face challenges in establishing robust cancer registries, due to the fixed and labor costs associated with developing these.³⁶ This paucity of data can result in inaccurate estimates of cancer incidence, with only 1 in 5 LMICs having sufficient quality data.³⁷ These gaps can hinder efforts to analyze trends to inform approaches for cancer control.³⁶

Case study

AU: Cancer screening registers

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Data security and privacy: Technologies, such as those which use AI and analyze large amounts of patient data, need strong governance over data sharing, security, and ownership,³⁸ due to the need to access sensitive health information.³⁹ Data should be shared responsibly and securely – through encryption, anonymization, and clear policies on data ownership – to build public trust, help patients retain control over their health data, and contribute to advances in research.⁴⁰

Case study

AU: Creating a healthcare data platform

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Data interoperability: The future of cancer care depends on a robust, interoperable data infrastructure that connects disparate data sources – such as data from governments, hospitals, academic institutions, wearable technologies, and consumer apps.⁴¹ This interconnected system is essential to harness the power of data in advanced clinical decision-making, empowering patients to be more involved in their care, streamlining the evaluation of treatments based on real-world data, and supporting value-based healthcare by simplifying cost assessment.^40,42

Case study

US: Facilitating data collection from electronic health records

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Fact sheet

System governance – Data and infrastructure

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Planning, transparency and accountability

Cancer plan framework: Every country should secure political commitment to establish its own long-term transformational project for cancer care, which aligns with major national health goals – whether that means charting a path towards curing cancer, building greater resilience and sustainability in healthcare, or reducing equity gaps in access and outcomes. Discussions with the expert group suggested that this could be supported by comprehensive horizon scanning, as occurs in Canada,⁴³ and a fully funded 10-to-15-year NCCP, equipped with robust implementation metrics and ongoing evaluation. To drive tangible progress, it was proposed that the plan should be implemented through a series of medium-term (3-to-5-year) action plans – ‘ground shots’ – which lay out the practical steps needed to achieve the long-term vision.

Case study

BR: National cancer committee/institute

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Case study

FR: Cancer Plan

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Case study

CN: Commitment to tackling cancer

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Accountability: Many existing NCCPs suffer from insufficient oversight and a lack of shared responsibility among key stakeholders.³⁰
A blended model of accountability, where elected officials, health system administrators, service providers, academic institutions, industry representatives, and patient groups collaboratively develop and execute a comprehensive cancer strategy, could help address these gaps whilst ensuring a diverse range of perspectives and expertise. For this to be effective a named cancer leader needs to be responsible for the development and adoption of the plan.

Case study

EU: Cancer Mission Hubs (ECHoS)

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Case study

EU: PCM4EU

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Case study

Global: QUAD cancer moonshot initiative

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Case study

NO & SE: Multi-stakeholder leadership

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Transparent monitoring and reporting: To maintain accountability and transparency, regular reporting on performance metrics should be a fundamental aspect of any framework.⁴⁴ Data should be collected to monitor key performance indicators for NCCPs, health systems, and specific care pathways. Actionable insights can be drawn, through the integration of AI, to inform the development and implementation of NCCPs, whilst regular performance reviews can foster greater public trust.⁴⁵ Real-time monitoring and reporting also aids equitable decision making, allowing elected officials and system administrators to reallocate resources where they are most needed. Several European Union Member States, through initiatives like Europe’s Beating Cancer Plan, have established annual reporting mechanisms to transparently report on progress against measurable targets.⁴⁶

Case study

EN & CH: Patient experience surveys

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Case study

Global: Cancer performance indicator

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Case study

HR: Cancer plan

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Building capability and capacity in healthcare infrastructure & workforce

Digitally assisted sampling and analysis: As technologies – such as WGS, liquid biopsy and AI assisted pathology – become better at informing early cancer detection and treatment, health systems will need to establish highly integrated and productive sampling, data collection and storage, analysis, monitoring, and communication capabilities to:^48,49

1. Collaborate with other experts in real time⁴⁹ and leverage global partnerships as seen between the UK and Australia in radiology diagnosis, providing an out of hours service⁵⁰
2. Generate diagnoses for rare and complex diseases⁵¹
3. Increase the quality and efficiency of diagnostics as well as facilitate patient engagement⁵²
4. Increase compliance with clinical practice guidelines, through integration of guidelines into electronic health record systems⁵³

An expanding role for primary care: Primary care providers are essential in identifying cancer symptoms, referring patients for specialist investigation and supporting lifestyle modification to reduce NCD risk.⁵⁴ As the focus on early detection intensifies, primary care’s role must expand, to incorporate more sophisticated tools like genetic profiling and AI-powered decision support systems to more efficiently refer high-risk patients to screening and specialized care.^55,56 To support this shift, training programs for primary care professionals must be reconfigured.⁵⁷ The role of healthcare professionals out-of-hospital can also extend to community care. In England, several cancer alliances carried out a pilot which allowed pharmacists to directly refer for scans patients they thought may have cancer.⁵⁸

Reconfiguration of workflows: As healthcare shifts towards more digital and technology-driven models⁵⁹ workflows must adapt. In pathology labs, for instance, the advent of digital pathology and AI-powered image analysis will fundamentally change the way labs operate.⁶⁰ New roles will emerge, such as technicians who manage and photograph digital slides for AI analysis,⁶¹ allowing for more efficient workflows and faster diagnoses, reducing the time patients wait for critical results. Supporting this transformation will require not just retraining, but also a reimagining of shift patterns and cross-jurisdictional collaboration.

Emerging role of data experts: As healthcare systems become more reliant on AI and machine learning to interpret vast amounts of patient data, roles such as data scientists, bioinformaticians, and AI specialists will become increasingly integral to day-to-day operations.⁶² These professionals can support clinicians by ensuring accurate data analysis and by helping to develop personalized treatment plans.

Upskilling patient navigators: Patient navigation will be key as care pathways become more complex with the introduction of advanced treatments, like immunotherapy and precision medicine. Patient navigators help individuals understand their diagnosis, treatment options, and follow-up care, guiding them through complex medical systems and ensuring they receive timely interventions.⁶³ As patients face being seen by multiple specialists, receiving multiple treatments and diagnostic tests, navigators will need to be well-versed not only in medical care but also in digital tools that help streamline communication between providers, schedule appointments, and track treatment progress.

Fact sheet

System governance – Workforce capacity

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Case study

NO: Cancer clusters to promote knowledge exchange

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Chapter 3: Personalized primary prevention

Cancer prevention focused on population-wide approaches addressing modifiable risk factors – such as tobacco control policies, dietary regulations and vaccination programs – has long been a cornerstone of public health efforts, driven by its proven cost-effectiveness and potential to significantly reduce the burden on healthcare systems.⁶⁴ Research has shown that the incremental cost-effectiveness ratios of prevention efforts for breast cancer was $25,000, $24,000 for colorectal cancer and $34,000 for prostate cancer.⁶⁵

However, approaches focused solely on modifiable risk factors do not fully account for all cancer deaths in the population.⁶⁶ For example, we now know that one in ten cancers may run in the family.⁶⁷ By 2040, our vision for cancer prevention efforts includes the use of advanced data and technology to empower individuals to actively reduce their personal risk from all risk factors. Following discussions with the expert group, we see this vision as needing to integrate clinical data, genetic profiles, and real-time personal health metrics – such as lifestyle behaviors and prior medical history – to allow for AI-driven predictive models that generate personalized prevention plans. However, as cancer prevention strategies are increasingly invested in and adopted across health systems, it is critical to ensure that these advancements do not exacerbate existing health inequalities.⁶⁸ Socioeconomic factors already play a major role in cancer survival, and without deliberate efforts to address disparities, personalized prevention could widen the gap.⁶⁹ In this chapter, we explore the evolution of cancer prevention, the role of technology in shaping its future, and the policy considerations needed to ensure equitable access to personalized prevention strategies.

Wearable-and-data-enabled risk assessment and lifestyle intervention

Wearables, like fitness trackers, allow people to have more control of their health wherever they are⁷⁰ and can be used to monitor location and lifestyle data, which could then be used by an AI tool to model a person’s cancer risk, identifying and alerting those most at risk.⁷¹ These capabilities, however are yet to be developed. Investing in affordable innovations like smartphone apps and wearables could also have the potential to make personalized prevention accessible to more people.⁷¹ While, today, wearables such as glucose monitoring devices may be difficult to access in LMICs,⁷² the high use of smartphones and the trend of technological innovations becoming cheaper⁷³ shows promise for widespread future use.⁷¹ As these technologies become more affordable, LMICs may be able to “leapfrog” some of today’s more inefficient interventions, which have been trialed in high income countries (HICs), in their prevention efforts. Furthermore, the challenge caused by digital exclusion and digital illiteracy, which tends to be more prominent in older people,⁷⁴ is likely to be lessened as younger, more digitally literate generations become a larger part of the population.

Better identification and understanding of inherited cancer risk

Genetic testing is an important new frontier for cancer prevention, as patients with known familial cancer risk often struggle to access effective interventions within a healthcare system primarily designed to treat, not prevent, cancer.⁷⁵ As the cost of genetic testing decreases and more genetic data becomes available, we will be able to identify even more genes that cause cancer (“oncogenes”) and the specific types of cancer they are linked to.⁷⁶ However, for genetic testing to truly benefit patients, it is essential to have robust genetic counselling services in place⁷⁷ to ensure patients have the support to understand their results, interpret the implications for their health, and make informed decisions about preventive measures.⁷⁷ By 2040, as knowledge of oncogenes expands, people with identified genetic risks may seek risk-stratified screening programs and effective, personalized cancer prevention interventions, supported by accessible genetic counselling.

Case study

Global: Accounting for genetic risk in breast cancer risk prediction models

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Case study

Global: Using machine learning to predict disease

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Population based whole genome sequencing (WGS)

WGS allows us to analyze multiple genes and create a person’s polygenic (or multi-gene) risk score, which captures part of an individual’s genetic susceptibility to cancer.⁷⁸ These risk scores could help streamline current screening programs to those with high polygenetic risk.⁷⁹ Before WGS can be offered to entire populations, we need to demonstrate why this technology can provide better data than family medical histories or previous genetic tests. Strict guidance will also be necessary on when WGS should be performed – for example, while some newborns are screened for rare diseases,⁸⁰ it might be more useful to test for cancer risk in young adults. Implementation of WGS should also consider the social, cultural and legal context of a country as genetic discrimination and stigmatization are feared by individuals with a genetic predisposition to cancer.⁸¹ While there are laws around genetic discrimination in some countries, such as the US, these are not necessarily widespread.^81,82 To ensure WGS benefits everyone, it must be implemented in a way that aligns with available resources. For LMICs, setting up large-scale genetic testing may not happen by 2040, given the lack of infrastructure and available funds.⁸³ So, while WGS has a lot of potential, countries will need to assess if adopting for their entire population is a long-term investment worth making.

Case study

SE: Use of WGS to determine cancer risk and diagnosis

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Case study

US: WGS of newborns

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Chapter 4: Early cancer detection and precision diagnosis

Progress has been made in early cancer detection and diagnosis,¹⁶ however too many individuals still receive diagnoses at advanced stages, when treatment options are more limited and significantly more costly.⁸⁴ In many cases, early-stage cancer presents without obvious symptoms, making timely diagnosis challenging.⁸⁵ To address this global challenge, initiatives such as the Global Breast Cancer Initiative and the Cervical Cancer Elimination Initiative are driving efforts to promote earlier detection and improve outcomes.^86,87

By 2040, we envision a world where all individuals at risk of developing cancer are proactively identified long before symptoms appear. Advances in genomic profiling, artificial intelligence, and digital pathology will help create personalized, efficient, and equitable diagnostic pathways, helping to ensure that high-risk individuals receive continuous monitoring, effective prevention strategies, and swift access to treatment.

However, as these innovations continue to evolve, it is essential to consider the challenges and risks associated with scaling up early detection programs, to ensure accuracy and accessibility for all populations. These include:

• Overdiagnosis, which may result in non-threatening conditions being detected and treated unnecessarily, leading to emotional, physical, and financial stress for patients.⁸⁸ As some cancers may be slowly progressive or not progressive at all,⁸⁹ overdiagnosis can lead to distorted survival rates and create the illusion of progress.
• Access disparities, such as when diagnostics are often not covered by insurance or require significant co-pays. In South Korea, NGS gene panel test requires 50% copay for advanced lung adenocarcinoma⁹⁰ and in the US lung cancer screening requires prior authorization from Medicare.⁹¹
• Quality control will become increasingly challenging as patient volumes increase.⁹²
• High costs of developing and implementing innovations may burden health systems when scaled to larger population coverage,⁹³ especially in LMICs, where there are larger funding gaps.⁹⁴
• Outmoded health technology assessment which relies too much on overall survival (OS) as a measure for Health Technology Assessments (HTA), becomes a challenge for cancers which have improved prognosis and are diagnosed early, delaying reimbursement.⁹⁵ This is discussed further in Chapter 7.

Fact sheet

Cancer care pathway: Diagnosis

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As we look to understand how we can make the 2040 vision a reality, we identified several current and emerging technologies which have the potential to both accelerate and improve the accuracy and precision of cancer detection through genomic profiling.⁹⁶ In this chapter, we explore the transformative potential of precision diagnostics and the key considerations for ensuring these advancements benefit patients globally.

New forms of early detection technology

AI-assisted digital radiology: AI is increasingly being integrated into digital radiology workflows to help doctors analyze images more quickly and accurately, increasing the volume of medical images that can be analyzed by one person.^97,98 This technology is also useful in LMICs, due to the potential lack of specialist workforce,⁹⁹ enabling faster, more accurate diagnosis. However, incomplete and poor-quality training data for AI models can lead to bias and inaccuracy in interpretation,¹⁰⁰ it is therefore imperative that the radiology workforce is upskilled on the use of these technologies, and that high quality data sets representative of the local population are used to train AI assistants.¹⁰¹

Case study

AU: Adoption of AI in radiology

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Total-body Positron Emission Tomography (PET) scans: This technology increases the geometric coverage of traditional PET scans which can increase sensitivity by a factor of approximately 40 for whole body imaging, or a factor of approximately 4 for single organ imaging, conferring greater ability to determine whether cancers have spread.¹⁰²

Volatile organic compounds (VOCs): VOCs are chemicals that can easily evaporate into the air, including through a person’s breath or other bodily fluids.¹⁰³ Researchers are exploring how the identification of these compounds in a sample given by a patient can serve as biomarkers to identify cancer earlier and non-invasively. This research is still in the early stages, and more studies are needed to confirm how reliable these methods are.¹⁰⁴

Liquid biopsy for early detection and Multi-Cancer Early Detection tests (MCEDs): While tissue biopsy is still considered the gold standard for diagnosis due to its consistency and accuracy,¹³² liquid biopsy offers advantages as a rapid, non-invasive way of diagnosing cancer.¹⁰⁵ Liquid biopsy detects different molecular markers in one’s blood or body secretions. These markers can reveal tumor-specific changes at an early stage, when tissue biopsy is not practical.¹⁰⁶ MCED tests are a type of liquid biopsy which look for many cancers using a single sample, however their accuracy and performance is still being determined.^107,108 The Liquid Biopsy Consortium is funding several studies around the efficacy of MCEDs for early cancer detection, although data so far suggests that its use as a screening method for colorectal cancer is not yet cost effective.¹⁰⁹

Case study

EN: Galleri Multi-Cancer Early Detection (MCED) trial

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Improving the models of early detection we have today

Expanding and strengthening existing screening programs: While many countries already offer screening programs, including for breast, cervical, colorectal¹¹⁰ and more recently lung cancer^111,112,113 participation is often low and varies across regions, income groups, and demographics.¹¹⁴ For example, breast cancer screening rates of women aged 50 to 69 in Europe in 2022 vary from 83% in Denmark, to 48% in France and 28.5% in Slovakia.¹¹⁵ To improve participation programs need to be culturally appropriate, include community outreach¹¹⁶ and become more risk-stratified. Technology can help this, through telehealth, mobile screening units, and home-based self-sampling, as seen during the COVID-19 pandemic.^{117,118,119,120} Text messages and telephone calls should also consider local cultural contexts,¹²¹ as has been trialed in Ghana for breast cancer screening.¹²² Furthermore, digital twins (virtual copies of patients)¹²³ could be used to risk-stratify screening approaches for high-risk populations, developing more personalized approaches.¹²⁴

Case study

CA & SE: At-home colorectal screening tests

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Fact sheet

Cancer care pathway: Screening and early detection

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Hub-and-spoke diagnostic models: Specialized diagnostic and treatment centers (the hubs) are connected to smaller, local healthcare facilities (the spokes) which provide initial care and screening, while digitized images or diagnostic samples are sent to the remote specialist pathology hubs for analysis.¹²⁵ Integration of telemedicine and mobile diagnostic units can increase capacity and broaden access to specialized diagnostic care, particularly for underserved populations in regions with limited healthcare infrastructure – for whom early detection services are currently difficult to access.^125,126

Case study

NG, BR & IN: Diagnostics hub-and-spoke model

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Community diagnostic centers (CDCs): CDCs provide diagnostics closer to home, including imaging and laboratory analyses, without needing to visit larger hospitals.¹²⁷ This reduces travel barriers and significantly increases participation, particularly among underserved populations.¹²⁷ CDCs can also streamline the diagnostic process, allowing for quicker results and facilitating timely referrals to specialists when needed.¹²⁷

Digital care pathways: These solutions often involve creating a tool to guide healthcare professionals through each step of the diagnostic process, from initial patient evaluation to final diagnosis and treatment of a suspected cancer.¹²⁸ They can help doctors offer guideline-concordant care, reduce delays, and improve communication between different members of a patient’s MDT, allowing them to offer more personalized care.¹²⁸

Point-of-care diagnostic (POC) devices: Innovative portable devices for POC testing are revolutionizing cancer diagnosis and management by offering immediate, accessible, and accurate testing solutions.¹²⁹ Unlike traditional diagnostic methods, which often require visits to specialized labs or hospitals and may involve long waiting times for results, POC devices enable testing to occur directly in community settings, primary care offices, or even in patients’ homes.¹³⁰ These portable devices can quickly detect cancer biomarkers through small samples such as blood, saliva, or urine, delivering results within minutes or hours rather than days.¹³¹

Molecular profiling and phenotyping as a standard part of diagnosis

Liquid biopsies: Beyond early detection, liquid biopsies can provide substantial information around diagnosis, including the potential aggressiveness of a tumor, as well as the proliferation, migration and drug resistance of cancer cells.¹³² However, work still needs to be done to refine their sensitivity and specificity.¹³³ Today, this technology isn’t readily available through routine care for all patients or even in different countries, due to funding and implementation challenges.¹³⁴ In the case of the UK, to support wider scale testing there is a need to invest in building the appropriate health system and laboratory infrastructure.¹³³ It is also key to demonstrate the potential cost effectiveness of this tool, compared to more traditional diagnostic methods. For example, in the UK, at a test cost of £100 there is approximately a 75% probability of a liquid biopsy test for brain cancer being cost-effective in primary care.¹³⁵ Cost savings in treatment selection have also been observed in lung cancer.¹³⁶

Proteomics and omics: Protein biomarkers are used in the early detection of several cancers and for ongoing monitoring or a response to a therapeutic intervention.¹³⁷ Advances in proteomics technology means thousands of previously unexplored proteins can be assessed for their association to different cancers.¹³⁸ This type of research can identify genetic variation within cancer cells of the same type of cancer, that traditional diagnostic methods might miss.¹³⁹

Digital pathology: Through digitization, high-resolution images of tumor samples can be easily shared across regions, allowing pathologists from around the world to collaborate in real-time to provide a detailed diagnosis, even in remote and low-income areas.⁶⁰ While this technology holds great promise, its widespread adoption requires significant investment in high-resolution scanners and data storage capacity. However, studies estimate cost savings of up to $12.4 million over 5 years, due to improvements in improvements in pathology productivity and histology lab consolidation.¹⁴⁰

Case study

UK: Use of digital pathology in cancer screening programs

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Computational pathology: Powered by AI and machine learning, computational pathology can further enhance the accuracy of digital pathology by analyzing digital images consistently and in a level of detail not visible to the human eye, helping to detect patterns or anomalies that humans may miss.^38,47,141 For example, a study shows that it can reduce the time taken to review a sample by as much as 30%, while improving accuracy.¹⁴² To enable its widespread use, there is a need to increase access to new AI tools, high-speed internet and robust cybersecurity; to restructure workflows to add an AI assistant, and to provide training on working with AI to help increase the productivity of laboratory professionals.¹⁴³

Quantum technologies: Quantum technologies in healthcare, including oncology care, is an emerging field with applications in precision diagnosis, including the analysis of genomic and omics data to reveal trends in cancer types.¹⁴⁴ Quantum Boltzmann machines can identify nonlinear relationships between different types of omics data, enhancing our ability to analyze large, multidimensional datasets in precision oncology.¹⁴⁴ Additionally, quantum technology can enhance medical imaging and provide comparable accuracy to current methods – researchers have used a quantum framework to analyze mammograms and automate the segmentation of images to identify abnormalities.¹⁴⁵ By minimizing the need for annotated datasets (used by existing techniques), this method is time saving and could accelerate time to diagnosis.¹⁴⁵

Monitoring (post-treatment surveillance)

MCED tests: MCEDs also hold the potential to detect recurrences in cancer survivors.¹⁴⁶ Although the MCED concept is still under investigation, there is potential for LMICs to ‘leapfrog’ more expensive alternatives with the eventual adoption of these technologies.¹⁴⁷

AI-driven predictive analytics: AI and machine learning can integrate diverse data sets to identify cancer recurrence using patterns that may otherwise be missed.^148,149 For example, AI can pair continuous monitoring of vital signs by wearable devices with genomic data and imaging to create personalized predictions.¹⁵⁰ As discussed earlier in Chapter 2, important ethical and regulatory issues need to be resolved before AI’s predictive capability can be safely and reliably maximized.

Case study

Global: Using AI to support chronic condition management

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Chapter 5: Personalized treatment

Over the past 30 years, cancer treatment has undergone a dramatic transformation with innovations in immunotherapy, targeted treatments, chemotherapy, radiotherapy, and surgical techniques. These advancements have made treatments more effective and less physically taxing on patients.^{151,152,153,154} In 2040, cancer treatment has the potential to go further – becoming fully personalized and equitable, offering patients optimal outcomes with minimal side effects by avoiding therapies that may not be effective for certain cancer types.¹⁵⁵

However, despite continuous innovation, progress in cancer outcomes has slowed due to persistent barriers in accessing novel technologies,^156,157 creating inequalities both within and between countries, regions, and cancer types.^158,159 In Europe, between 2019 and 2022, the median time to patient access to medicines varied from 1.5 months to over 2 years post market authorization.¹⁶⁰ In LMICs, high costs, limited healthcare infrastructure, and shortages of specialized healthcare professionals further restrict access to the latest therapies, exacerbating disparities in cancer care.¹⁶¹

Emerging technologies and new models of care hold the potential to close these gaps and make personalized, augmented cancer treatment a reality. These are described in further detail within this chapter.

Fact sheet

Cancer care pathway: Treatment

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Continuous treatment innovation

Targeted therapy: Today’s targeted therapies address many of the most common cancer mutations, which are used as biomarkers for targeted therapies. In Japan, the SCRUM-Japan GOZILA Project found patients who received targeted therapy based on the results of a liquid biopsy lived approximately twice as long as their peers.¹⁶² Because a cancer cell mutates further as it tries to adapt and survive a treatment, ongoing innovation is needed in targeted therapy to address the most common “resistance mutations”.¹⁶³ To improve the outcomes from targeted therapy, ongoing genomic testing can be utilized to guide and adjust treatment and counteract the evolving nature of the cancer.¹⁶⁴

• DNA damage response: Every cell in our body needs to continuously repair the wear and tear of its DNA from everyday use.¹⁶⁵ When oncogenes are mutated in the DNA of a tumor cell, that DNA is unable to repair itself.¹⁶⁵ This is called a “repair deficiency” and can be used as a tool for targeted therapy.¹⁶⁶ These treatments limit a cell’s ability to repair its DNA, thereby killing cancer cells with repair deficiencies.¹⁶⁷

Radiotherapy: Over the past decade we have seen evolution in radiotherapy technology, with enhanced ability to precisely target tumors while minimizing damage to surrounding health tissues.¹⁶⁸ New treatment strategies include hypofractionation which involves fewer but more intensive radiation sessions, and exploration of novel combinations with other traditional cancer therapies, such as immunotherapy.^169,170

• MR-LINACs: This method of radiotherapy integrates MRI imaging into lineal accelerators (LINACs) to allow MRI-guided radiation therapy, which ensures safer delivery and the ability to monitor tissues while the radiotherapy is being delivered.¹⁷¹ This might lead to a reduction in treatment time and less side effects for patients.¹⁷² In 2018, the Royal Marsden hospital in London treated the first UK patient with MR-LINAC therapy, helping to roll it out on a more global scale.¹⁷³
• Radioimmunotherapy: Recently, publications on radioimmunotherapy have increased, as the benefit of combining these two cancer treatments shows promise.¹⁷⁴ Studies indicate the combined use of radiotherapy and immunotherapy can be more effective than either treatment alone.¹⁷⁵ Its use has been investigated non-small cell lung cancer, esophageal and gastroesophageal junction tumors.¹⁷⁶

Multimodal conjugates:

• Antibody-drug conjugates (ADCs): These treatments conjugate (or link) a special protein called an antibody that binds easily to some cells (biomarkers) with a cancer-fighting chemotherapy drug.¹⁷⁷ The antibody is designed to find and attach to cancer cells expressing a matching biomarker on their surface. Once the antibody attaches to the cancer cell, it delivers the drug directly inside, where it can effectively kill it. This limits the damage to healthy cells and, with some ADCs, helps to kill “bystander” tumor cells that threaten the patient but do not express a treatable biomarker.¹⁷⁷
• Radioconjugates: These therapies conjugate radioactive materials and special targeting molecules to attack cancer cells.¹⁷⁸ The radioactive part releases radiation that can kill cancer cells, while the targeting molecules help guide the treatment directly to the tumor, sparing nearby healthy tissue.¹⁷⁸

Immunotherapies:

• Bispecific immunotherapies: These two-arm treatments work by targeting two different proteins at the same time, helping the immune system recognize and attack cancer cells more effectively.¹⁷⁹ One arm of the bispecific antibody binds to a biomarker on the cancer cell while the other arm attaches to immune cells, helping the immune cell reach its target.¹⁷⁹
• CAR-T cell therapies: A patient’s immune cells, called T-cells, are modified in a lab to make them better at finding and attacking cancer cells.¹⁸⁰ These therapies offer shorter treatment times and quicker recovery, although they require complex cell processing that limits access to them.^181,182 These therapies can be autologous (using the patient’s own immune cells) or allogenic (using cells from a donor).¹⁸³ Allogenic therapies provide an ‘off the shelf’ treatment as genetically engineered donor cells are stored in a cell bank waiting to be used.
• Cancer vaccines: These are designed to help the body’s immune system recognize and attack cancer cells.¹⁸⁴ While existing preventive vaccines address infections like hepatitis B or HPV that may cause cancer, new types of cancer vaccines will directly treat cancers.¹⁸⁴ Much like today’s immunotherapies, they will train the immune system to target and fight cancer cells.¹⁸⁴

Epigenetic therapeutics: While genetic alterations are permanent, epigenetic alterations can be reversed.¹⁸⁵ Epigenetic changes tend to result from exposure to known risk factors like smoking and chronic inflammation stemming from causes such as hepatitis B.¹⁸⁶ Genetics and epigenetics work together to turn healthy cells into cancer cells.¹⁸⁷ New therapies are in development to re-program cancer cells back to healthy cells, whilst also holding the potential to improve the efficacy of existing anti-cancer treatments.^185,188

Surgical techniques: The landscape of cancer surgery has been revolutionized in recent years, predominantly due to advancements in minimally invasive and precision surgical technology.¹⁸⁹ Robotic technologies have aided surgical techniques, providing enhanced control and visual precision, resulting in less trauma to tissues, shorter stays in hospital, lower infection risks and fewer surgical complications.¹⁹⁰ For example, when undergoing prostatectomy for prostate cancer, patients can leave the hospital the day following surgery.¹⁹¹

Drug repurposing: Developing new cancer therapies and technologies is a time consuming and expensive endeavor. Drug repurposing uses existing drugs for new indications and provides an exciting opportunity to circumvent these challenges and facilitate precision cancer treatment.¹⁹² Repurposing drugs as anticancer agents has been explored in prostate cancer for example.¹⁹³

Case study

EU: Precision Cancer Medicine Repurposing System Using Pragmatic Clinical Trials, PRIME-ROSE

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Digital twins: Digital twins are virtual replicas of patients, based on the integration of several sets of data from medical imaging, genomics and other clinical data points.¹⁹⁴ Digital twins can act as predictive tools to understand how patients may respond to different types of treatment to make the most effective and safe treatment decisions.¹⁹⁴ However, there are important considerations around the use of healthcare data for the development of digital twins, including obtaining informed consent, ensuring data ownership and navigating legal limitations.¹⁹⁵ These aspects of healthcare data are further discussed in Chapter 2.

Case study

US: Innovative detection and treatment tools for cancer care in the US

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Equity in access to innovation

To derive the benefits of these innovations, health systems must prepare for their rapid adoption, through the modernization of clinical trial protocols, regulatory frameworks, health technology assessment, and payment models.

Access to clinical trials: Limited access to clinical trials disproportionately affects minority populations, who are more likely to receive care at under-resourced hospital systems where few clinical trials are available.¹⁹⁶ Since diversity in clinical trial participants is critical to precision medicine, broadening participation benefits not only patient participants but also the companies running the trials.¹⁹⁷ To improve equity of access, trials must also take place in rural and remote areas, with new approaches to disseminate recruitment information, investment in infrastructure, and less stringent eligibility criteria that allow a wider range of people to participate, especially those with complex comorbidities.¹⁹⁸ This disparity in access is not just observed within countries, but between HICs and LMICs, with only 43% of clinical trials for diseases that disproportionately impact people living in LMICs being carried out in LMICs.¹⁹⁹

Fact sheet

Cancer care pathway: Clinical trials

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Case study

APAC: Decentralized access to clinical trials

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Case study

CN, JP, EU & US: Expanding access to clinical trials

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Case study

US: Health equity report card

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Regulatory sandboxes for AI: Ensuring that AI systems are reliable and accurate across diverse patient populations is complex, especially given the potential for biased or incomplete datasets.²⁰⁰ The “black box” nature of many AI models, where decision-making processes are not easily understood, adds further complexity.²⁰¹ Since regulators tend to require a high level of transparency for approval, existing regulatory frameworks are not suited to the fast-evolving nature of AI technologies, leading to delays in their approval.²⁰² Privacy and data security concerns, especially when handling sensitive patient information, also present significant hurdles.²⁰³ New regulatory sandboxes, where government supervise the deployment of new technology in the real world, in small-scale pilots, could help to speed up the adoption of new AI technologies. ²⁰⁴

Case study

UK: Regulatory sandbox on AI

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Hub-and-spoke clinical trial model: A hub will collect information from all its spokes, enabling trials to cast the participant recruitment net much wider and potentially increasing the speed of research and development, especially for rarer cancers.²⁰⁵

Digital and telemedicine in clinical trials: Digital platforms and telemedicine can help mitigate geographical inequities by allowing patients to participate in trials without needing to travel to major research centers.²⁰⁶ Mobile health apps, wearable devices, and remote monitoring, also present opportunities to collect more accurate and complete personal health information from clinical trial participants remotely.²⁰⁷ Virtual recruitment tools, such as online registries, electronic health records and AI-driven matching algorithms, can help identify eligible patients more efficiently.^208,209,210 AI can also enhance drug selection and adapt investigational drugs to the histology of a specific cancer, thus increasing efficacy.²¹⁰ While this is an area of fast-paced innovation, there are important ethical concerns around data availability, standards, and a lack of appropriate regulatory frameworks that inhibit the speed at which AI can be used in drug development.²¹⁰

Chapter 6: Holistic long-term support

Today, many individuals living with cancer face significant barriers to reintegrating into their normal lives during or after treatment, due to the wide reaching side effects of treatment.²¹¹ Survivorship care must go beyond the current focus on clinical treatment and standard care²¹² and access to less toxic therapies, to address the full spectrum of patient needs – physical, emotional, psychological, social, and financial. A fundamental shift is required to provide cancer survivors with person-centered, long-term support that helps them reclaim their personal, professional, and broader aspirations.^213,214

Survivorship care should include ongoing monitoring for disease recurrence (as discussed in Chapter 4), managing long-term side effects, and providing access to psychological support, lifestyle guidance, and financial assistance.^214,215 Many survivors also struggle with “financial toxicity,” requiring career counseling and workplace accommodations to maintain employment while managing health challenges.^214,216 Future care models must embrace technology, telemedicine, real-time digital health monitoring, and decentralized care to improve accessibility and reduce dependence on specialized centers.^217,218

By 2040, AI-powered tools, telemedicine platforms, and wearable devices will have the potential to transform survivorship care to provide continuous, personalized support beyond treatment. However, for these innovations to be effective, governments and healthcare systems must invest in holistic survivorship programs alongside novel treatments. Without this commitment, cancer’s impact will continue to extend far beyond diagnosis, affecting survivors’ ability to fully reintegrate into daily life and society.²¹⁹ In this chapter, we explore how these technological enablers, along with new care delivery models, can shape the future of survivorship, providing cancer survivors with the comprehensive, long-term support needed to truly thrive.

Improved models of care delivery

Improvements in patient centricity: Patient-centric care remains largely aspirational in today’s healthcare systems, where many patients experience fragmented care and limited involvement in decision-making.²²⁰ In the future, technology will be key in making true patient-centric care a reality. Mobile apps, telemedicine platforms, and wearable devices can enable continuous, real-time interaction between patients and healthcare providers,²²¹ allowing patients direct access to their health data and enabling active feedback loops.²²² However, as discussed in Chapter 3, challenges like digital literacy, equitable access to technology, and disparities in care quality across different populations must be addressed.²²³

Case study

CA: Adoption of electronic pathway solutions

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Case study

US: National Cancer Institute’s Community Oncology Research Program (NCORP)

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Patient navigation programs: As survivors transition from active treatment to survivorship, they often face a complex healthcare landscape involving less cancer specialists, more primary care, ongoing screenings, a diverse set of rehabilitation providers, and permanent changes to their bodies that they may continue to struggle with physically and emotionally.²²⁴ Patient navigation programs provide personalized guidance to help survivors better understand the range of services available to them – with a review analysis showing they are crucial in improving access to care and optimizing treatment outcomes, especially for disadvantaged patients.^63,225 These navigators assist with scheduling appointments, coordinating follow-up care, addressing financial concerns, and connecting patients to psychosocial support.^63,225 However, they need to be tailored to specific needs, as research indicates that, for example, young people may have differing preferences depending on their developmental age.²²⁶

Case study

EU: App for cancer survivors

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Case study

US: American Cancer Society (ACS) patient navigation support

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Case study

Global: Digital emotional support tool

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Case study

UK: Cancer Research UK’s support forum

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Digital-and-AI-powered MDTs: MDT support has become a fundamental component of cancer care across most of Europe, North America, and Australia.²²⁷ However, barriers such as time constraints, heavy caseloads, and incomplete patient information can lead to suboptimal treatment.²²⁷ By 2040, MDTs must routinely include oncologists, geneticists, primary care physicians, mental health professionals, nutritionists, social workers, palliative care doctors, and data scientists to collaborate in real time to deliver efficient holistic support.²²⁸ This should leverage digital platforms to securely share patient data in real-time, streamlining communication between providers^229,230 and AI tools to provide decision support to clinicians.²³¹ Finally, telemedicine and remote monitoring technologies can enable more frequent information exchange between the MDT and patients, allowing MDTs to provide continuous, personalized support regardless of geographic location.^229,232

Case study

Global: NAVIFY Tumour Board

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Palliative care: Good quality end of life care for cancer patients is vital to avoid unnecessary suffering for both patients and their families. However, substandard palliative care is common due to a lack of structured guidelines.²³³ By 2040, palliative care should incorporate innovation and technology. Models which utilize digital health and community-based health workers have been shown to achieve positive outcomes, particularly in rural areas.²³⁴ Smart sensor technologies can collect comprehensive data and gain a greater understanding of a patient’s condition235 and provide continuous, less invasive symptoms management.²³⁵

Psychosocial support

Psychosocial telemedicine platforms: Telemedicine can enhance access by reducing barriers such as travel distance or scheduling conflicts for specialist care, especially for rural or underserved populations.²³⁶ This can also save costs for patients – with a US study showing that telehealth can save up to USD$186 per visit.²³⁷ These platforms can also be made available 24/7. Most clinicians interviewed during a survey agreed that telehealth increases access to (91.7%), and simplifies the process for patients to receive (79.2%) early integrated palliative care.²³⁸ Services such as emotional support, financial aid, education, and rehabilitation can all be offered online. For instance, virtual support groups, AI-powered counselling apps, chatbots, and online financial navigation are helping patients overcome barriers in their treatment journey.^239,240,241

Case study

US: Memorial Sloan Kettering’s telemedicine platform

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Economic and employment support

Adaptive and assistive technologies: Devices that mitigate cognitive and physical side-effects and impairments that patients have during and/or post-treatment, such as ergonomic tools or AI-powered software, can help people living with cancer improve their performance at work and at home.^242,243 These technologies can also offer real-time monitoring, allowing employers to adjust workloads and expectations based on the survivor’s health.¹⁵⁰

Employment support: This can include vocational retraining, flexible work arrangements, and other workplace accommodations.²⁴⁴ Digital platforms and AI tools can assist survivors by matching them with appropriate job opportunities and providing tailored career counselling based on their health status and professional goals.²⁴⁵ Furthermore, employers can help by implementing supportive policies, such as phased returns to work and remote working options.²⁴⁴

Case study

CA & NL: Supporting employment of cancer survivors

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Financial support: Providing financial support not only alleviates the burden on patients but also improves health outcomes, by enabling patients to focus on their recovery rather than financial concerns.²⁴⁶ This includes financial assistance programs, subsidies for medications, and coverage for non-medical costs like transportation and caregiving.²⁴⁷ AI-powered tools could help assess a patient’s financial situation and match them with applicable assistance programs, grants, or subsidies for both medical and non-medical expenses.^247,248 Survivors could receive real-time notifications about available aid, including coverage for transportation or caregiving costs, tailored to their specific needs.

Case study

US: Financial support for cancer patients

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Blockchain-based healthcare wallets: This technology could streamline medical payments, ensuring transparency and security in transactions while reducing administrative costs.²⁴⁹ Additionally, digital platforms could help streamline access to financial aid for cancer survivors by integrating various resources into user-friendly, centralized systems.

Preventing financial discrimination: Many cancer survivors face discrimination in areas such as employment, insurance, and socialization, which can lead to feelings of isolation and hinder recovery and reintegration post-treatment.²⁵⁰ Legislation on genetic non-discrimination and the right to be forgotten both have the potential to mitigate financial toxicity for cancer survivors who can find themselves shut out of the labor market, denied health insurance, and refused bank loans.^251,252 For example, France has introduced the right to be forgotten law, which gives cancer survivors the right to not disclose their past cancer diagnosis to medical insurers.²⁵³ France was the first country in the world to introduce such a law,²⁵⁴ since then only seven other European countries have implemented similar measures.²⁵⁴

Chapter 7: Cancer funding and financing

Cancer care financing is one of the most pressing challenges in global health, as the growing cancer burden and rising costs of innovation put immense pressure on healthcare systems worldwide. Financial toxicity affects patients in both HICs²⁵⁵ and LMICs, where limited resources and high out-of-pocket expenses create significant barriers to care.²⁵⁶ Alarmingly, only 27% of NCCPs include financing strategies.³¹ Often those NCCPs which do include resource allocation in NCCPs, lack detail around costing and calculations of funding amounts, which makes evaluating the effectiveness of these and their impact on cancer control challenging.²⁵⁷ Without strategic reforms, disparities in access to cancer prevention, early detection, and treatment might continue to widen, exacerbating health inequalities across regions.

By 2040, a transformation in cancer care financing will be essential to ensure equitable access to life-saving treatments and robust funding for preventive interventions. This will require fundamental reforms in HTA processes to enable cost-effective innovations to reach patients more efficiently. Integrating innovative financing mechanisms – such as outcome-based payment models, risk-sharing agreements, and public-private partnerships – alongside long term initiatives such as universal health coverage, is imperative for long term sustainability and improving patient care.²⁷² In this section, we explore the evolving landscape of cancer financing, outlining key challenges and opportunities to build a resilient, patient-centered funding ecosystem.

Status quo of cancer financing globally

Cancer care financing differs significantly across countries, reflecting disparities in economic capacity and exacerbating inequality in cancer care.²⁵⁸ Global health priorities have also changed, for example, initiatives such as the sustainable development goals, which does include indicators on non-communicable diseases, has led to HICs such as the UK focusing funding on universal health coverage and supporting major third-party organizations like Gavi, rather than increasing funding for cancer control.²⁵⁹

HICs typically allocate a higher percentage of their gross domestic product (GDP) to healthcare. Public health spending ranges from 11.5% of GDP in HICs, to 5.3% in lower to middle income countries²⁶⁰ – a figure that might drop even further in LICs, where 5% is recommended as an aspirational target.²⁶¹ In LMICs, due to these constrained budgets, access to both innovative and off patent essential cancer treatments is often limited by high costs and underdeveloped health insurance systems.²⁶²

Middle-income countries, such as Brazil, India, and South Africa, face distinct challenges as they work toward sustainable cancer financing.²⁶³ While these nations are experiencing economic growth, which is expected to contribute to enhanced healthcare infrastructure, access to medical services and improved cancer detection, it might also lead to a deepening in healthcare inequalities.²⁶³ These countries must balance domestic healthcare spending with economic growth and evolving disease burdens.

Approaches to sustainable cancer financing

Innovative financing models

As the financial burden of cancer care continues to rise and innovative treatments become more complex, efficient resource allocation is essential to ensuring sustainable and equitable access to treatments. Innovative financing models offer promising solutions that can help healthcare systems manage limited funds more effectively while maintaining high-quality care delivery. These innovative models include:

• Payment-over-time models: These models allow the total cost of a therapy to be spread out over a year or several years, in installments.²⁶⁴ This is particularly useful for high-cost, one-time treatments like gene therapy.²⁶⁵ This is already a reality in some centers in Italy and at the level of individual sick funds in Germany.²⁶⁴
• Outcomes-based payment agreements: In this model, payers agree to pay a price that is based on the results of future data, for example, as part of an ongoing clinical trial or as part of a post-marketing observational study (real-world evidence).²⁶⁴ A UK study found that data is frequently available to support agreements based on clinical outcomes, such as survival and disease progression, but that there is still limited ability to pay based on quality-of-life indications – such as ability to return to work, participate in social activities, reduce long-term side effects, and maintain financial health.²⁶⁶
• Innovative medicines funds: These financial models are designed to ensure patients have access to cutting-edge therapies, even when their efficacy and cost-effectiveness are still under review.²⁶⁷ These funds, also referred to as ring-fenced cancer drugs funds, allow for early patient access to innovative medicines, while real-world data is collected to inform long-term funding decisions.²⁶⁷ For example, the UK established a fund of £680 million for the Innovative Medicines Fund (IMF)²⁶⁸ and Cancer Drugs Fund (CDF)²⁶⁹ – £340 million each – to fast-track access to innovative medicines.

Case study

IT: Innovative medicines fund

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Case study

UK: Innovative medicines fund

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• Public-private partnerships (P3s): In some cases, governments partner with private companies on transformative initiatives.²⁷⁰ P3s allow governments to provide access to quality services without the need for large capital investments, as private and/or philanthropic organizations can handle construction, equipment procurement, and training.²⁷⁰ These partnerships can be anchored in outcome-based payment models, key performance indicators, and pay-for-performance schemes that incentivize the delivery of a high standard of care. These partnerships should aid a balanced approach to cancer financing which can prioritize prevention and early diagnosis alongside treatment, to reduce long term costs from late-stage diagnoses.¹⁵

Case study

BR, TH & IN: Licensing agreements

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Case study

CA: Health impact investments

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Case study

CO: Global financing initiatives

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• Pay per use models: For innovative technologies in oncology care such as robotic surgery or radiotherapy equipment, pay-per-use contracts may provide a more affordable way of expanding access for patients by transforming high upfront capital costs into more manageable operational expenses.²⁷¹
• Development impact bonds: These are an instrument where private investors provide initial funding for a program, and an outcome funder reimburses it back if targeted outcomes are met as a result of the investment.²⁷² There has been an interest in these bonds to fund responses to NCDs and healthcare initiatives globally, for example in India.²⁷³

Case study

IN: Development impact bonds

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A key advantage of these models is their ability to accelerate access and rollout of life-saving therapies, by linking payments to measurable health outcomes. This ensures high-cost treatments like gene therapies are more accessible while balancing financial risk between payers and manufacturers. Additionally, these models might complement existing funding mechanisms, extending public budgets through risk-sharing agreements and private-sector investment. This integrated approach enhances sustainability and expands access to critical cancer treatments.²⁷⁴

Nonetheless, the feasibility and scalability of these innovative financing models depend on several factors, including market readiness, infrastructure requirements and teams who can lead implementation.²⁷⁴ High-income countries with advanced healthcare systems may be better positioned to implement complex models like outcomes-based reimbursement or tiered pricing. In contrast, LMICs may benefit from simpler mechanisms, such as pooled procurement or public-private partnerships, which leverage shared resources to achieve cost efficiencies.

Rethinking and adapting HTA methodologies

HTAs are essential for guiding healthcare funding decisions, helping policymakers determine which treatments should be covered and reimbursed.²⁷⁵ However, traditional HTA frameworks often focus on overall survival (OS) as the main measure of a cancer drug’s effectiveness.²⁷⁶ While this makes sense for advanced cancers, where survival might be measured within a few years, it poses challenges for early-stage cancers, where patients may live for decades before the full benefit of a treatment becomes clear.²⁷⁷ As a result, many promising innovations may be undervalued simply because long-term survival data is not yet available.

Adapting HTA methodologies would not only improve access to life-saving oncology treatments but also spark investment in areas where innovation has the greatest potential to transform outcomes.²⁷⁸ By prioritizing patient-centered outcomes and the full spectrum of clinical benefits, these methodologies can drive sustainable financing decisions and ensure that patients benefit from the latest innovations in cancer care. Potential novel HTA approaches could include:

• Oncology-relevant endpoints: By 2040, payers will need to find a way to effectively value other endpoints – such as progression-free survival (PFS), disease-free survival (DFS), event-free survival (EFS), and the pathological complete response (shrinkage) of a tumor (pCR).²⁷⁹ Health-related quality-of-life (HRQoL) data, especially patient-reported outcomes (PROs), can also provide an early indication that a treatment works.²⁸⁰ There is also work that can be done to go beyond quality-adjusted life years (QALYs), to understand the quality and quantity of life lived following the administration of a specific treatment and fully capture the experience of cancer patients and elements which may be important to them, such as how a treatment is delivered or how invasive it is.²⁸¹
• Lifetime value: HTA must begin to appreciate the full range of benefits a medicine or health technology can have on both society and on patients.²⁸² Additional value elements which should be incorporated include the impact of disease on the broader communities than patients, which can include productivity – the reduction in losses of the market and non-market output from treatment of the disease.²⁸² Incorporating these elements could enable HTA bodies to help governments understand the full and true value of reimbursement of innovative therapies.
• Optimize integration of Real-World Evidence (RWE): RWE can play a key role in regulatory decision making, and offer a nuanced real world understanding of the performance of interventions beyond traditional clinical trial settings, such as randomized controlled trials.²⁸³ With bodies like the National Institute for Health and Care Excellence (NICE) in England outlining frameworks for its using RWE²⁸⁴ and calls to increase the use of RWE for HTA in Asia,²⁸⁵ its acceptability in HTA is increasing. However, there are still barriers to its use including limited data access, data quality, privacy and fragmentation.²⁸⁶ More comprehensive and reliable healthcare data collection at a national level will be required.
• Limited focus on diagnostic innovations: HTA tends to be most advanced and established for pharmaceuticals, rather than medical technologies and diagnostics. ^287,288 This is despite the fact diagnostics and medical tests could improve patient outcomes through improving accuracy at reduced costs and simplify healthcare delivery through access, diagnostic yield and reduction in time to diagnosis.²⁸⁸ In oncology, for example, liquid biopsies have the potential to have a significant impact on care by identifying cancers early.¹⁸ However, there are challenges in evaluating diagnostic tests for HTA as a result of reliance on indirect rather than direct evidence of effect.²⁸⁹ By evolving HTA methodologies to value innovation, including diagnostics, through multicriteria decision analysis and tools such as horizon scanning, healthcare systems can accelerate the adoption of these groundbreaking technologies.²⁸⁹
• Static cost-effectiveness thresholds: Many HTA systems use rigid cost-effectiveness thresholds that fail to account for healthcare innovation’s dynamic and evolving nature.²⁹⁰ There are a number of dynamic value elements which could aid a more comprehensive HTA.²⁸² This includes dynamic net health system costs which considers how the costs of a drug’s price may change and become cheaper due to generics, dynamic disease prevalence account for changes in the prevalence of a condition affect treatment value. Real option value represents the value of living long enough to take advantage of future innovations and scientific spillover accounts for the value of expected innovation which can be triggered by the development of a treatment.²⁸² Adapting HTA cost-effectiveness thresholds presents an opportunity to better capture the long-term value of innovative therapies.

Health infrastructure funding

Health infrastructure funds can provide the necessary resources to develop the infrastructure needed for effective cancer care. These can be set up on a national or multilateral level:

• National funds exist for healthcare infrastructure, such as the New Hospital Programme in the UK.²⁹¹ However they can often face hurdles, such as the limitations imposed by annual healthcare budgets and the allocation of funding which is at times not delivered.²⁹¹

Case study

EE: Innovation infrastructure fund

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Case study

NG: Infrastructure fund

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• Multilateral funding options could also be explored, to create a collaborative effort to strengthen healthcare systems, with multilateral development banks often financing healthcare infrastructure.²⁹² However loans can be limited at times to distinct geographies, as is the case with the World Bank, which only provides funding for health to LMICs and may be dependent on specific pre-conditions.²⁹³

Regional funding mechanisms

Regional funding mechanisms for cancer, such as the Europe’s Beating Cancer Plan²⁹⁴ and the EU Cancer Mission,²⁹⁵ are crucial for addressing complex health challenges by pooling resources and expertise across Member States. They allocate significant funding to cancer-related activities and enable large-scale research projects, implementation of innovative prevention and treatment strategies and development of infrastructure and technologies.^296,297

Case study

EU: Europe’s Beating Cancer Plan

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Case study

EU: The EU Cancer Mission

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Other funding for ecosystems and technology clusters

Unlocking non-health-specific investments also presents a promising avenue for enhancing funding in cancer care. Investors increasingly seek opportunities in sustainability²⁹⁸ and AI projects,²⁹⁹ recognizing their potential for significant social impact. By framing cancer care innovations within the context of these high-demand sectors, stakeholders could potentially attract funding from existing innovation funds traditionally reserved for non-health applications.

Case study

FR: Funding for AI

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Chapter 8: Strategic recommendations for future proofing cancer care

This chapter outlines key strategic recommendations for health systems and policymakers to shape the future of cancer control. By addressing critical areas such as innovation adoption, healthcare infrastructure, equitable access, and economic sustainability, these recommendations aim to guide effective policy decisions and drive transformative changes in cancer care, ensuring better outcomes for all populations.

Short term actions

• Expand the role of primary care to increase early detection by integrating more advanced diagnostic tools in the primary care setting and training general practitioners to equip them with the necessary knowledge.
• Expand multidisciplinary cancer care by leveraging AI decision support systems, machine-learning tools, and care navigators. These innovations can enable timely referrals, adherence to guideline-directed therapy, and early detection of high-risk patients while also shifting certain tasks to primary care providers, pharmacists, and specialist nurses.
• Improve data interoperability by harmonizing data standards across different healthcare settings and countries to facilitate seamless integration of digital tools which can be used to support precision care.

Medium to long term actions

• Invest in the recruitment and training of data scientists, AI engineers, and digital health experts to drive the integration of AI-powered tools, precision medicine, and real-world data analytics in cancer care.
• Prepare the workforce for the reconfiguration of workflows as healthcare shifts towards technology-driven models through reimagining shift patterns and cross-jurisdictional collaboration.
• Enhance predictive analytics and risk stratification models to anticipate service demand, optimize resource allocation, and identify populations needing intensified screening or follow-up.

Short term actions

• Establish a multistakeholder national cancer governance body with a clear mandate to oversee NCCP implementation. This body should track progress and drive accountability through performance monitoring frameworks with transparent clear key performance indicators and data-driven reporting mechanisms.
• Accelerate the incorporation of AI-powered imaging and pathology tools into existing cancer screening and diagnosis programs, improving early detection accuracy and workflow efficiency.
• Develop holistic lifetime support programs, powered by digital health tools and community-based care, to help cancer survivors reintegrate into their personal and professional lives.

Medium to long term actions

• Expand laboratory and digital infrastructure to prepare for the routine use of liquid biopsy, genetic testing, WGS and other emerging technologies in early detection and personalized treatment selection.
• Incentivize research and development of new diagnostic and treatment modalities, by fostering collaborations between governments, research institutions, and private sector.
• Build a secure, robust and AI-ready data infrastructure that integrates real-world data across care settings, organizations and clinical modalities to support clinical trials, observational studies, and patient monitoring.

Short term actions

• Encourage collaboration between governments, the private sector and healthcare systems to enable seamless global participation in clinical trials, ensuring they are more accessible in all countries. This can be enabled through decentralized clinical trial models.
• Expand equitable access to cancer care by decentralizing it. For example, scale up mobile screening units, enable home-based self-sampling, create digital pathology networks, and conduct culturally appropriate community outreach.
• Accelerate regulatory approvals for novel cancer technologies by incorporating surrogate endpoints such as minimal residual disease and patient-reported outcomes, alongside traditional overall survival measures.

Medium to long term actions

• Establish ethical and regulatory frameworks for the safe, transparent, and effective use of AI and digital health technologies in cancer care, ensuring equitable access and patient data protection.
• Rework HTA frameworks to incorporate real-world evidence from a robust post-marketing surveillance infrastructure that may include registries and/or monitoring tools to assess long-term treatment benefits and societal impact.
• Adopt value-based payment models that prioritize long-term benefits over upfront costs, ensuring reimbursement systems recognize the broader value of early, curative, and precision-based interventions.

Acknowledgements

The development of this report has been funded and led by AstraZeneca, with the support and consultation of an expert group in the field of oncology. The expert group provided input throughout the process, including guidance on the paper’s direction, content, and recommendations. AstraZeneca would like to thank the contributors for their insights, as without whom, this work would not have been possible. Some of these experts received honoraria for their time as per local regulations and have been listed below, in alphabetical order.

External contributors

Andrea Feigl, Founder and CEO, Health Finance Institute

Dr Cary Adams, Chief Executive Officer, Union for International Cancer Control

Ebba Hallersjö Hult, Co-Founder and Head, Vision Zero Cancer

Dr Gilberto Lopes, Professor of Clinical Medicine, Medical Director for International Programmes, Associate Director for Global Oncology, Chief, Division of Medical Oncology, Sylvester Comprehensive Cancer at the University of Miami

Dr Ian Walker, Executive Director of Policy Information and Communications, Cancer Research UK

Richard Vines, Chair and Founder of International Programmes, Rare Cancers Australia

Šarūnas Narbutas, Founding Partner at Minds, Co-Founder and Chairman at Youth Cancer Europe, Chief Advisor at Lithuanian Cancer Patient Coalition, CAB member and ex-Chairman at CML Advocates Network

Internal contributors

Dr Bob Li, Global Head of Medical Affairs, Oncology, AstraZeneca

Chris McPherson, Head of Global Oncology Policy & Advocacy, AstraZeneca

Dave Fredrickson, Executive Vice-President, Oncology Business, AstraZeneca

Felicia Flowitt, Director of Oncology Policy (Pan-Tumour), AstraZeneca

Shira Gerver, Vice President, Global Head of Oncology Corporate Affairs, AstraZeneca

Stefan Weber, Vice President Global Policy, Advocacy & Health Equity, AstraZeneca

Dr Sunil Verma, Senior Vice President, Global Head, Oncology Franchise, AstraZeneca

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