Histopathologic image-based deep learning classifier for predicting platinum-based treatment responses in high-grade serous ovarian cancer.
Nat Commun
; 15(1): 4253, 2024 May 18.
Article
em En
| MEDLINE
| ID: mdl-38762636
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.
Texto completo:
1
Base de dados:
MEDLINE
Assunto principal:
Neoplasias Ovarianas
/
Platina
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Cistadenocarcinoma Seroso
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Aprendizado Profundo
Limite:
Adult
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Aged
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Female
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Humans
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Middle aged
Idioma:
En
Revista:
Nat Commun
Assunto da revista:
BIOLOGIA
/
CIENCIA
Ano de publicação:
2024
Tipo de documento:
Article
País de afiliação:
Coréia do Sul