AI test predicts breast cancer recurrence ^{CEFR B2}

Despite improvements in breast cancer care, the risk of recurrence remains an important problem for patients and clinicians. A study in Nature Communications describes a multi-modal artificial intelligence test that aims to predict which patients are most likely to see their cancer return. The authors note the test can work faster and more cheaply than current genomic tests, which often take weeks and use tissue that is later discarded.

The research was led by Krzysztof J. Geras, a visiting scholar at NYU’s Center for Data Science and an adjunct assistant professor at NYU Grossman School of Medicine; Yann LeCun, a NYU professor and co-author, emphasised that self-supervised pretraining helped the model learn useful representations prior to final prediction. The AI combines routine clinical data with pathology slides to produce risk estimates.

The clinical information used included tumor stage, patient age and hormone-receptor status. The team evaluated the approach across 15 patient populations in seven countries and tested it on more than 3,500 patients. They assessed performance with standard measures such as the C-Index and a Hazard Ratio. The AI distinguished higher-risk from lower-risk patients, performed well for triple-negative and HER2-positive breast cancers, and in the authors’ evaluations matched or outperformed a widely used genomic test.

The researchers emphasise the need for evaluation in completed randomized clinical trials before the test can guide treatment decisions. The paper notes potential conflicts: some authors are equity holders of Ataraxis AI; Krzysztof J. Geras is co-founder and chief scientific officer of Ataraxis AI, and New York University maintains financial and intellectual property interests in the company. Source: New York University.