| Autor: |
Fremond, Sarah, Andani, Sonali, Barkey Wolf, Jurriaan, Dijkstra, Jouke, Melsbach, Sinéad, Jobsen, Jan J, Brinkhuis, Mariel, Roothaan, Suzan, Jurgenliemk-Schulz, Ina, Lutgens, Ludy C H W, Nout, Remi A, van der Steen-Banasik, Elzbieta M, de Boer, Stephanie M, Powell, Melanie E, Singh, Naveena, Mileshkin, Linda R, Mackay, Helen J, Leary, Alexandra, Nijman, Hans W, Smit, Vincent T H B M, Creutzberg, Carien L, Horeweg, Nanda, Koelzer, Viktor H, Bosse, Tjalling |
| Zdroj: |
The Lancet Digital Health; February 2023, Vol. 5 Issue: 2 pe71-e82, 12p |
| Abstrakt: |
Endometrial cancer can be molecularly classified into POLEmut, mismatch repair deficient (MMRd), p53 abnormal (p53abn), and no specific molecular profile (NSMP) subgroups. We aimed to develop an interpretable deep learning pipeline for whole-slide-image-based prediction of the four molecular classes in endometrial cancer (im4MEC), to identify morpho-molecular correlates, and to refine prognostication. |
| Databáze: |
Supplemental Index |
| Externí odkaz: |
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