For the first time, scientists have applied machine learning, a form of artificial intelligence (AI), to identify the factors most closely linked to cancer survival in nearly every country across the globe.
The research, publishedin the leading cancer journal Annals of Oncology, goes beyond broad comparisons to show which specific policy changes or system improvements could have the greatest impact on cancer survival in each nation. The team has also created an online tool that allows users to select a country and see how factors such as national wealth, access to radiotherapy, and universal health coverage relate to cancer outcomes.
Turning Global Data Into Practical Insights
Dr. Edward Christopher Dee, a resident physician in radiation oncology at Memorial Sloan Kettering (MSK) Cancer Center in New York, USA, and a co-leader of the study, highlighted why the work matters. “Global cancer outcomes vary greatly, largely due to differences in national health systems. We wanted to create an actionable, data-driven framework that helps countries identify their most impactful policy levers to reduce cancer mortality and close equity gaps.”
He noted that several factors consistently stood out. “We found that access to radiotherapy, universal health coverage and economic strength were often important levers being associated with better national cancer outcomes. However, other key factors were relevant as well.”
Analyzing Cancer and Health System Data From 185 Countries
To reach these conclusions, Dr. Dee and his colleagues used machine learning to examine cancer incidence and death data from the Global Cancer Observatory (GLOBOCAN 2022), covering 185 countries. They combined this information with health system data gathered from the World Health Organization, the World Bank, United Nations agencies, and the Directory of Radiotherapy Centres.
The dataset included health spending as a percentage of GDP, GDP per capita, the number of physicians, nurses, midwives, and surgical workers per 1000 people, levels of universal health coverage, access to pathology services, a human development index, the number of radiotherapy centers per 1000 people, a gender inequality index, and the share of healthcare costs paid directly by patients.
Building the Machine Learning Model
The machine learning model was developed by Mr, Milit Patel, the study’s first author. He is a researcher in biochemistry, statistics and data science, healthcare reform and innovation at the University of Texas at Austin, USA, and at MSK.
Mr, Patel explained the reasoning behind this approach. “We chose to use machine learning models because they allow us to generate estimates – and related predictions – specific to each country. We are, of course, aware of the limitations of population level data but hope these findings can guide cancer system planning globally.”
Measuring Cancer Care Effectiveness
The model calculates mortality-to-incidence ratios (MIR), which represent the share of cancer cases that result in death and serve as an indicator of how effective cancer care is in a given country. To show how individual factors influence these estimates, the researchers used a method that explains predictions by measuring each variable’s contribution, known as SHAP (Shapley Additive exPlanations).
According to Mr. Patel, the goal was to move from description to action. “Beyond simply describing disparities, our approach provides actionable, data-driven roadmaps for policymakers, showing precisely which health system investments are associated with the greatest impact for each country. As the global cancer burden grows, these insights can help nations prioritize resources and close survival gaps in the most equitable and effective way possible. International organizations, healthcare providers, and advocates may also use the web-based tool to highlight areas for investment, especially in resource-limited settings.”
Country Examples Show Different Priorities
The results reveal that the most influential factors vary widely by country. In Brazil, the model indicates that universal health coverage (UHC) has the strongest positive association with improved mortality-to-incidence ratios. Other factors, such as pathology services and the number of nurses and midwives per 1000 people, appear to play a smaller role at present. The researchers suggest this means Brazil could see the greatest gains by prioritizing UHC.
In Poland, the availability of radiotherapy services, GDP per capita, and the UHC index show the largest impact on cancer outcomes. This pattern suggests that recent efforts to expand health insurance and access to care have produced stronger improvements than general health spending, which appears to have a more limited effect.
Japan, the USA, and the UK show a broader pattern, with nearly all health system factors linked to better cancer outcomes. In Japan, the density of radiotherapy centers stands out most strongly, while in the USA and the UK, GDP per capita has the greatest influence. These findings point to where policymakers in each country may achieve the biggest gains.
China presents a more mixed picture. Higher GDP per capita, broader UHC, and greater access to radiotherapy centers contribute most to improved cancer outcomes. By contrast, out-of-pocket spending, the size of the surgical workforce per 1000 people, and health spending as a percentage of GDP currently explain less of the variation in outcomes.
The researchers write about China: “High direct costs for patients remain a critical barrier to optimal cancer outcomes, even amidst national improvements in health financing and access. These findings underscore that while China’s rapid health system development is yielding important gains in cancer control, disparities in financial protection and coverage persist, warranting intensified policy focus on reducing out-of-pocket expenditures and further strengthening UHC implementation to maximize health system impact.”
How to Read the Green and Red Bars
Mr, Patel also explained the meaning of the green and red bars shown in the country-specific graphs. “The green bars represent factors that currently appear most strongly and positively associated with improved cancer outcomes in a given country. These are areas where continued or increased investment is most likely to result in meaningful impact.”
He stressed that red bars should not be misunderstood. “However, the red bars do not indicate that these areas are unimportant or should be neglected. Rather, they reflect domains that, according to the model and current data, are less likely to explain the largest differences in outcomes right now. This may be due to already strong performance in these aspects, limitations of the available data, or other context-specific factors.”
He added an important caution. “Importantly, seeing a ‘red’ bar should never be interpreted as a reason to stop efforts to strengthen that pillar of cancer care – improvement in those areas can still be valuable for a country’s overall health system. Our results simply suggest that, if the goal is to maximize improvement in cancer outcomes as defined by the model, focusing first on the strongest positive (green) drivers may be the most impactful strategy.”
Strengths, Limits, and What Comes Next
The study’s strengths include its coverage of nearly all countries, use of current global health data, country-specific policy guidance rather than simple global averages, and the use of more transparent AI models. The researchers also acknowledge key limitations. The analysis relies on national-level data rather than individual patient records, data quality varies widely, especially in many low-income countries, and national trends can hide disparities within countries. In addition, the study cannot prove that focusing on a specific factor will cause better cancer outcomes, only that such efforts are associated with improved results.
Even with these limits, the findings offer a useful way to prioritize action. Dr. Dee concluded: “As the global cancer burden grows, this model helps countries maximize impact with limited resources. It turns complex data into understandable, actionable advice for policymakers, making precision public health possible.”
