Synapse - Multimodal histopathologic models stratify hormone receptor-positive early breast cancer

Multimodal histopathologic models stratify hormone receptor-positive early breast cancer Journal Article

Authors:	Boehm, K. M.; El Nahhas, O. S. M.; Marra, A.; Waters, M.; Jee, J.; Braunstein, L.; Schultz, N.; Selenica, P.; Wen, H. Y.; Weigelt, B.; Paul, E. D.; Cekan, P.; Erber, R.; Loeffler, C. M. L.; Guerini-Rocco, E.; Fusco, N.; Frascarelli, C.; Mane, E.; Munzone, E.; Dellapasqua, S.; Zagami, P.; Curigliano, G.; Razavi, P.; Reis-Filho, J. S.; Pareja, F.; Chandarlapaty, S.; Shah, S. P.; Kather, J. N.
Article Title:	Multimodal histopathologic models stratify hormone receptor-positive early breast cancer
Abstract:	The Oncotype DX® Recurrence Score (RS) is an assay for hormone receptor-positive early breast cancer with extensively validated predictive and prognostic value. However, its cost and lag time have limited global adoption, and previous attempts to estimate it using clinicopathologic variables have had limited success. To address this, we assembled 6172 cases across three institutions and developed Orpheus, a multimodal deep learning tool to infer the RS from H&E whole-slide images. Our model identifies TAILORx high-risk cases (RS > 25) with an area under the curve (AUC) of 0.89, compared to a leading clinicopathologic nomogram with 0.73. Furthermore, in patients with RS ≤ 25, Orpheus ascertains risk of metastatic recurrence more accurately than the RS itself (0.75 vs 0.49 mean time-dependent AUC). These findings have the potential to guide adjuvant therapy for high-risk cases and tailor surveillance for patients at elevated metastatic recurrence risk. © The Author(s) 2025.
Keywords:	adult; controlled study; aged; middle aged; genetics; histopathology; area under the curve; recurrence risk; sensitivity analysis; cell proliferation; biological marker; metabolism; neoplasm recurrence, local; pathology; breast neoplasms; tumor marker; high risk patient; patient care; nomograms; laboratory test; tumor recurrence; breast tumor; bioassay; tumor cell; receptors, estrogen; receptors, progesterone; area under curve; estrogen receptor; progesterone receptor; health risk; hormone; hormone receptor; nomogram; resource allocation; receiver operating characteristic; tumor microenvironment; numerical model; machine learning; cancer; humans; prognosis; human; female; article; hormone receptor positive breast cancer; deep learning; biomarkers, tumor; large language model; confusion matrix
Journal Title:	Nature Communications
Volume:	16
ISSN:	2041-1723
Publisher:	Nature Publishing Group
Date Published:	2025-03-02
Start Page:	2106
Language:	English
DOI:	10.1038/s41467-025-57283-x
PUBMED:	40025017
PROVIDER:	scopus
PMCID:	PMC11873197
DOI/URL:	https://www.scopus.com/inward/record.uri?eid=2-s2.0-85219614038&doi=10.1038%2fs41467-025-57283-x&partnerID=40&md5=8f81486d5f9fec234e8bc3b95fae66eb
Notes:	The MSK Cancer Center Support Grant (P30 CA008748) is acknowledge in the PDF -- Corresponding authors is MSK author: Sarat Chandarlapaty and Sohrab P. Shah -- Source: Scopus