Multimodal histopathologic models stratify hormone receptor-positive early breast cancer
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 ha...
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| Hauptverfasser: | , , , , , , , , , , , , , , , , , , , , , , , , , , , |
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| Dokumenttyp: | Article (Journal) |
| Sprache: | Englisch |
| Veröffentlicht: |
02 March 2025
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| In: |
Nature Communications
Year: 2025, Jahrgang: 16, Pages: 1-14 |
| ISSN: | 2041-1723 |
| DOI: | 10.1038/s41467-025-57283-x |
| Online-Zugang: | Verlag, kostenfrei, Volltext: https://doi.org/10.1038/s41467-025-57283-x Verlag, kostenfrei, Volltext: https://www.nature.com/articles/s41467-025-57283-x 
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| Verfasserangaben: | Kevin M. Boehm, Omar S.M. El Nahhas, Antonio Marra, Michele Waters, Justin Jee, Lior Braunstein, Nikolaus Schultz, Pier Selenica, Hannah Y. Wen, Britta Weigelt, Evan D. Paul, Pavol Cekan, Ramona Erber, Chiara M.L. Loeffler, Elena Guerini-Rocco, Nicola Fusco, Chiara Frascarelli, Eltjona Mane, Elisabetta Munzone, Silvia Dellapasqua, Paola Zagami, Giuseppe Curigliano, Pedram Razavi, Jorge S. Reis-Filho, Fresia Pareja, Sarat Chandarlapaty, Sohrab P. Shah & Jakob Nikolas Kather |
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| 245 | 1 | 0 | |a Multimodal histopathologic models stratify hormone receptor-positive early breast cancer |c Kevin M. Boehm, Omar S.M. El Nahhas, Antonio Marra, Michele Waters, Justin Jee, Lior Braunstein, Nikolaus Schultz, Pier Selenica, Hannah Y. Wen, Britta Weigelt, Evan D. Paul, Pavol Cekan, Ramona Erber, Chiara M.L. Loeffler, Elena Guerini-Rocco, Nicola Fusco, Chiara Frascarelli, Eltjona Mane, Elisabetta Munzone, Silvia Dellapasqua, Paola Zagami, Giuseppe Curigliano, Pedram Razavi, Jorge S. Reis-Filho, Fresia Pareja, Sarat Chandarlapaty, Sohrab P. Shah & Jakob Nikolas Kather |
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| 520 | |a 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. | ||
| 650 | 4 | |a Breast cancer | |
| 650 | 4 | |a Data integration | |
| 650 | 4 | |a Image processing | |
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