| Autor: |
I. Garberis, V. Gaury, C. Saillard, D. Drubay, K. Elgui, B. Schmauch, A. Jaeger, L. Herpin, J. Linhart, M. Sapateiro, F. Bernigole, A. Kamoun, E. Bendjebbar, A. de Lavergne, R. Dubois, M. Auffret, L. Guillou, I. Bousaid, M. Azoulay, J. Lemonnier, M. Sefta, A. Jacquet, A. Sarrazin, J-F Reboud, F. Brulport, J. Dachary, B. Pistilli, S. Delaloge, P. Courtiol, F. André, V. Aubert, M. Lacroix-Triki |
| Rok vydání: |
2022 |
| Popis: |
BackgroundCorrectly classifying early estrogen receptor-positive and HER2-negative (ER+/HER2) breast cancer (EBC) cases allows to propose an adapted adjuvant systemic treatment strategy. We developed a new AI-based tool to assess the risk of distant relapse at 5 years for ER+/HER2-EBC patients from pathological slides.Patients and MethodsThe discovery dataset (GrandTMA) included 1429 ER+/HER2-EBC patients, with long-term follow-up and an available hematoxylin-eosin and saffron (HES) whole slide image (WSI). A Deep Learning (DL) network was trained to predict metastasis free survival (MFS) at five years, based on the HES WSI only (termed RlapsRisk). A combined score was then built using RlapsRisk and well established prognostic factors. A threshold corresponding to a probability of MFS event of 5% at 5 years was applied to dichotomize patients into low or high-risk groups. The external validation, as well as assessment of the additional prognosis value of the DL model beyond standard clinico-pathologic factors were carried out on an independent, prospective cohort (CANTO,NCT01993498) including 889 HES WSI of ER+/HER2-EBC patients.ResultsRlapsRisk was an independent prognostic factor of MFS in multivariable analysis adjusted for established clinico-pathological factors (pConclusionsOur deep learning model developed on digitized HES slides provided additional prognostic information as compared to current clinico-pathological factors and has the potential of valuably informing the decision making process in the adjuvant setting when combined with current clinico-pathological factors. |
| Databáze: |
OpenAIRE |
| Externí odkaz: |
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