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

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 and Eunhyang Park ()
Additional contact information
Byungsoo Ahn: Yonsei University College of Medicine
Damin Moon: JLK Inc.
Hyun-Soo Kim: Sungkyunkwan University School of Medicine
Chung Lee: Yonsei University College of Medicine
Nam Hoon Cho: Yonsei University College of Medicine
Heung-Kook Choi: JLK Inc.
Dongmin Kim: JLK Inc.
Jung-Yun Lee: Yonsei University College of Medicine
Eun Ji Nam: Yonsei University College of Medicine
Dongju Won: Yonsei University College of Medicine
Hee Jung An: CHA University School of Medicine
Sun Young Kwon: Keimyung University School of Medicine
Su-Jin Shin: Yonsei University College of Medicine
Hye Ra Jung: Keimyung University School of Medicine
Dohee Kwon: Yonsei University College of Medicine
Heejung Park: Yonsei University College of Medicine
Milim Kim: Yonsei University College of Medicine
Yoon Jin Cha: Yonsei University College of Medicine
Hyunjin Park: Yonsei University College of Medicine
Yangkyu Lee: Yonsei University College of Medicine
Songmi Noh: CHA University College of Medicine
Yong-Moon Lee: Dankook University School of Medicine
Sung-Eun Choi: CHA University School of Medicine
Ji Min Kim: Ewha Womans University
Sun Hee Sung: Ewha Womans University
Eunhyang Park: Yonsei University College of Medicine

Nature Communications, 2024, vol. 15, issue 1, 1-13

Abstract: 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.

Date: 2024
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DOI: 10.1038/s41467-024-48667-6

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