Machine learning models predict common cancer risks using routine health data

Machine learning models using routinely collected health information, such as blood tests, medical history and lifestyle factors, could help identify people at higher risk of several common cancers, according to an international study reported at the ESMO Congress 2026.

Researchers developed approximately 500 machine learning models to predict risk across 20 common cancers, using data from 463,620 UK Biobank participants who were followed for approximately 18 years. During this period, 57,055 participants developed cancer. The models analyzed information including medical history, lifestyle factors, blood tests, genetic and metabolomic data.

To assess how well the models performed in different populations, researchers tested them using data from participants in Scotland and Wales, as well as 42,036 people from the US All of Us research programme and more than 3 million from Japan's JMDC database. To reduce the influence of undiagnosed disease, cancers diagnosed within the first year were excluded from the relevant models.

The models outperformed traditional statistical approaches, although performance varied across cancer types. For a combined cancer outcome covering cancers that could be predicted from routine clinical data, they achieved an area under the curve (AUROC) of 0.83 at 15 years, where 0.5 is no better than chance and 1.0 is perfect prediction. Performance was particularly strong for lung, oesophageal and liver cancers, but more limited for ovarian, breast and cervical cancers. For acute myeloid leukaemia and multiple myeloma, performance declined when looking further ahead, suggesting some short-term predictions may reflect early signs of cancer that was already present but undiagnosed.

Much of the predictive information came from routine blood tests and clinical history. Blood tests continued to add predictive value for cancers diagnosed more than five years later, while genetic and metabolomic data added only a small further improvement. The team also developed PRE-Screen, a toolbox enabling artificial intelligence (AI) agents based on large language models (LLMs) to run the risk models consistently. Agents using it performed substantially better than LLMs making predictions unaided.

For selected cancers, these models could serve as a low-cost pre-screening step using data already available in routine care. Existing screening programmes cover only certain cancers and often use broad eligibility criteria, rather than assessing each person's individual risk."

Dr. Paul-Henry Koop, lead author, Else Kröner Fresenius Center for Digital Health, TU Dresden, Germany

In the European Union, recommendations have traditionally focused on breast, cervical and colorectal cancer, with the Council of the EU also recommending exploration of screening for other cancers. 

"Cancer types differ substantially in their carcinogenic mechanisms and in how strongly their risk factors and precursor conditions are captured in routine data," explained Koop. He noted that the models could help target screening or interventions to reduce cancer risk, but cautioned that many people flagged as higher risk would not develop cancer. "A positive flag should therefore trigger minimally invasive testing first," he said. Since the study analyzed existing data retrospectively, further research is needed to establish whether the approach leads to earlier diagnosis or better outcomes.

"What is needed is a comprehensive assessment of the proposed protocol in various kinds of populations and settings, external to the UK Biobank cohort," said Dr Marco Lorenzi, Research Director at Inria Sophia Antipolis, Université Côte d'Azur, France, who was not involved in the study. He emphasized that further validation was needed to assess the reliability and generalizability of the findings and potential biases.

"The automated analysis protocol proposed in this work may represent a valuable instrument for patient prioritization, early detection and risk assessment," Lorenzi suggested. "The benefit would be alleviating the burden of care on society and the healthcare system."