دورية أكاديمية

Histopathologic image–based deep learning classifier for predicting platinum-based treatment responses in high-grade serous ovarian cancer

التفاصيل البيبلوغرافية
العنوان: Histopathologic image–based deep learning classifier for predicting platinum-based treatment responses in high-grade serous ovarian cancer
المؤلفون: Byungsoo Ahn, Damin Moon, Hyun-Soo Kim, Chung Lee, Nam Hoon Cho, Heung-Kook Choi, Dongmin Kim, Jung-Yun Lee, Eun Ji Nam, Dongju Won, Hee Jung An, Sun Young Kwon, Su-Jin Shin, Hye Ra Jung, Dohee Kwon, Heejung Park, Milim Kim, Yoon Jin Cha, Hyunjin Park, Yangkyu Lee, Songmi Noh, Yong-Moon Lee, Sung-Eun Choi, Ji Min Kim, Sun Hee Sung, Eunhyang Park
المصدر: Nature Communications, Vol 15, Iss 1, Pp 1-13 (2024)
بيانات النشر: Nature Portfolio, 2024.
سنة النشر: 2024
المجموعة: LCC:Science
مصطلحات موضوعية: Science
الوصف: Abstract Platinum-based chemotherapy is the cornerstone treatment for female high-grade serous ovarian carcinoma (HGSOC), but choosing an appropriate treatment for patients hinges on their responsiveness to it. Currently, no available biomarkers can promptly predict responses to platinum-based treatment. Therefore, we developed the Pathologic Risk Classifier for HGSOC (PathoRiCH), a histopathologic image–based classifier. PathoRiCH was trained on an in-house cohort (n = 394) and validated on two independent external cohorts (n = 284 and n = 136). The PathoRiCH-predicted favorable and poor response groups show significantly different platinum-free intervals in all three cohorts. Combining PathoRiCH with molecular biomarkers provides an even more powerful tool for the risk stratification of patients. The decisions of PathoRiCH are explained through visualization and a transcriptomic analysis, which bolster the reliability of our model’s decisions. PathoRiCH exhibits better predictive performance than current molecular biomarkers. PathoRiCH will provide a solid foundation for developing an innovative tool to transform the current diagnostic pipeline for HGSOC.
نوع الوثيقة: article
وصف الملف: electronic resource
اللغة: English
تدمد: 2041-1723
Relation: https://doaj.org/toc/2041-1723
DOI: 10.1038/s41467-024-48667-6
URL الوصول: https://doaj.org/article/4942df33baf24489bbfb6bc5ad4292e7
رقم الأكسشن: edsdoj.4942df33baf24489bbfb6bc5ad4292e7
قاعدة البيانات: Directory of Open Access Journals
الوصف
تدمد:20411723
DOI:10.1038/s41467-024-48667-6