Prediction of recurrence risk in endometrial cancer with multimodal deep learning

Volinsky-Fremond, S; Horeweg, N; Andani, S; Barkey Wolf, J; Lafarge, MW; de Kroon, CD; &#216;rtoft, G; H&#248;gdall, E; Dijkstra, J; Jobsen, JJ; Lutgens, LCHW; Powell, ME; Mileshkin, LR; Mackay, H; Leary, A; Katsaros, D; Nijman, HW; de Boer, SM; Nout, RA; de Bruyn, M; Church, D; Smit, VTHBM; Creutzberg, CL; Koelzer, VH; Bosse, T

Author(s): Volinsky-Fremond, S; Horeweg, N; Andani, S; Barkey Wolf, J; Lafarge, MW; de Kroon, CD; Ørtoft, G; Høgdall, E; Dijkstra, J; Jobsen, JJ; Lutgens, LCHW; Powell, ME; Mileshkin, LR; Mackay, H; Leary, A; Katsaros, D; Nijman, HW; de Boer, SM; Nout, RA; de Bruyn, M; Church, D; Smit, VTHBM; Creutzberg, CL; Koelzer, VH; Bosse, T;
Details: Publication Year 2024-05-24,Volume 30,Issue #7,Page 1962-1973
Journal Title: Nature Medicine
Publication Type: Research article
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.
Publisher: Springer Nature
Department(s): Medical Oncology
Publisher's Version: https://doi.org/10.1038/s41591-024-02993-w
Open Access at Publisher's Site: https://doi.org/10.1038/s41591-024-02993-w
Terms of Use/Rights Notice: Refer to copyright notice on published article.

Creation Date: 2024-07-25 05:42:23

Last Modified: 2024-07-25 05:52:23