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Nat. Commun., May 18 2024, 15 (1) | https://doi.org/10.1038/s41467-024-48667-6 Histopathologic image-based deep learning classifier for predicting platinum-based treatment responses in high-grade serous ovarian cancer

Byungsoo Ahn ^1,13, Damin Moon ^2,13, Hyun-Soo Kim ^3,13, Chung Lee¹, Nam Hoon Cho ¹, Heung-Kook Choi ², Dongmin Kim², Jung-Yun Lee ⁴, Eun Ji Nam ⁴, Dongju Won ⁵, Hee Jung An ⁶, Sun Young Kwon ⁷, Su-Jin Shin ⁸, Hye Ra Jung ⁷, Dohee Kwon ¹, Heejung Park ¹, Milim Kim ¹, Yoon Jin Cha ^8,9, Hyunjin Park ⁸, Yangkyu Lee ⁸, Songmi Noh ¹⁰, Yong-Moon Lee ¹¹, Sung-Eun Choi ⁶, Ji Min Kim ¹², Sun Hee Sung¹² & Eunhyang Park¹

¹Department of Pathology, Severance Hospital, Yonsei University College of Medicine, Seoul, South Korea.

²Artificial Intelligence Research Center, JLK Inc., Seoul, South Korea.

³Department of Pathology and Translational Genomics, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, South Korea.

⁴Department of Obstetrics and Gynecology, Institute of Women’s Life Medical Science, Yonsei University College of Medicine, Seoul, South Korea.

⁵Department of Laboratory Medicine, Yonsei University College of Medicine, Seoul, South Korea.

⁶Department of Pathology, CHA Bundang Medical Center, CHA University School of Medicine, Seongnam, South Korea.

⁷Department of Pathology, Keimyung University School of Medicine, Daegu, South Korea.

⁸Department of Pathology, Gangnam Severance Hospital, Yonsei University College of Medicine, Seoul, South Korea.

⁹Institute of Breast Cancer Precision Medicine, Yonsei University College of Medicine, Seoul, South Korea.

¹⁰Department of Diagnostic Pathology, Gangnam CHA Medical Center, CHA University College of Medicine, Seoul, South Korea.

¹¹Department of Pathology, Dankook University School of Medicine, Cheonan, South Korea.

¹²Department of Pathology, Ewha Womans University, Seoul, South Korea.

¹³These authors contributed equally: Byungsoo Ahn, Damin Moon, Hyun-Soo Kim.

Corresponding author

Correspondence to Eunhyang Park.

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.

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형식Research article
게재일2024년 05월 (BRIC 등록일 2024-05-20)
연구진국내 연구진
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