Prediction of recurrence risk in endometrial cancer with multimodal deep learning

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 BruynDavid Church, Vincent T H B M Smit, Carien L Creutzberg, Viktor H Koelzer, Tjalling Bosse*

*Corresponding author af dette arbejde

Abstract

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. Here we 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 from eight EC cohorts including the PORTEC-1/-2/-3 randomized trials. HECTOR demonstrated C-indices in internal (n = 353) and two external (n = 160 and n = 151) test sets of 0.789, 0.828 and 0.815, respectively, outperforming the current gold standard, and identified patients with markedly different outcomes (10-year distant recurrence-free probabilities of 97.0%, 77.7% and 58.1% for HECTOR low-, intermediate- and high-risk groups, respectively, by Kaplan-Meier analysis). 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 on the current gold standard and may help delivery of personalized treatment in EC.

OriginalsprogEngelsk
TidsskriftNature Medicine
ISSN1078-8956
DOI
StatusE-pub ahead of print - 24 maj 2024

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