Prediction of recurrence risk in endometrial cancer with multimodal deep learning

Predicting distant recurrence of endometrial cancer (EC) is crucial for personalized adjuvant treatment. The current gold standard of combined pathological and molecular profiling is costly, hampering implementation. This study developed HECTOR (histopathology-based endometrial cancer tailored outcome risk), a multimodal deep learning prognostic model using hematoxylin and eosin-stained whole-slide images and tumor stage as input, on 2,072 patients. HECTOR demonstrated superior C-indices compared to the gold standard across internal and external test sets and identified patients with markedly different outcomes. HECTOR also predicted adjuvant chemotherapy benefit better than current methods. Morphological and genomic feature extraction identified correlates of HECTOR risk groups, some with therapeutic potential. HECTOR improves upon the current gold standard and may aid personalized treatment in EC.

Sarah Volinsky-Fremond, Nanda Horeweg, Sonali Andani, Jurriaan Barkey Wolf, Maxime W. Lafarge, Cor D. de Kroon, Gitte Ørtoft, Estrid Høgdall, Jouke Dijkstra, Jan J. Jobsen, Ludy C. H. W. Lutgens, Melanie E. Powell, Linda R. Mileshkin, Helen Mackay, Alexandra Leary, Dionyssios Katsaros, Hans W. Nijman, Stephanie M. de Boer, Remi A. Nout, Marco de Bruyn, David Church, Vincent T. H. B. M. Smit, Carien L. Creutzberg, Viktor H. Koelzer, Tjalling Bosse