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

Author

Listed:
  • 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
    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)

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.

Suggested Citation

  • 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 , 2024. "Histopathologic image–based deep learning classifier for predicting platinum-based treatment responses in high-grade serous ovarian cancer," Nature Communications, Nature, vol. 15(1), pages 1-13, December.
    • Handle: RePEc:nat:natcom:v:15:y:2024:i:1:d:10.1038_s41467-024-48667-6
    DOI: 10.1038/s41467-024-48667-6
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    References listed on IDEAS

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    1. Kun-Hsing Yu & Ce Zhang & Gerald J. Berry & Russ B. Altman & Christopher Ré & Daniel L. Rubin & Michael Snyder, 2016. "Predicting non-small cell lung cancer prognosis by fully automated microscopic pathology image features," Nature Communications, Nature, vol. 7(1), pages 1-10, November.
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