ArticleNature communications2024
Histopathologic image-based deep learning classifier for predicting platinum-based treatment responses in high-grade serous ovarian cancer.
Article in Nature communications, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 37 papers.
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Who cites it
37 citing papers in PubMed.
- Generative AI, foundation models and large language models in radiation therapy physics: Clinical applications, challenges, and future directions.Medical physics · 2026Review
- A unified vision-language model for precision oncology and biomarker prediction in neuroblastoma.Nature communications · 2026Article
- A Novel Approach to Ovarian Cancer Diagnosis via CT Imaging: GPT-4o-Driven Automated Feature Recognition and Validation in Clinical Settings.Annals of surgical oncology · 2026Article
- Deep learning-based prediction of gene expression from histopathology identifies NR5A1 as a candidate biomarker and druggable target in high-grade serous ovarian carcinoma.Journal of ovarian research · 2026Article
- A leakage-aware entropy screening protocol for structured biomarker panel evaluation in ovarian cancer risk modelling.MethodsX · 2026Article
- Deep Learning-Based Diagnosis of Epithelial Ovarian Cancer from Whole-Slide Histopathology Images.Diagnostics (Basel, Switzerland) · 2026Article
- Platinum-resistant ovarian cancer: From mechanisms to treatment strategies.Genes & diseases · 2026Review
- Distinction between primary and metastatic mucinous ovarian carcinoma from histopathology images using deep learning.NPJ digital medicine · 2026Article
- The consensus molecular subtypes of esophageal squamous cell carcinoma.Signal transduction and targeted therapy · 2026Article
- HRD-Informed Digital Histology Model for Predicting Platinum Chemo-Response and Prognosis in High-Grade Serous Ovarian Cancer.Interdisciplinary sciences, computational life sciences · 2026Article
- Artificial intelligence (AI) and machine learning (ML) in ovarian cancer: transforming detection, treatment, and prevention.Journal of ovarian research · 2026Review
- Artificial intelligence in digital pathology diagnosis and analysis: technologies, challenges, and future prospects.Military Medical Research · 2026Review
- Multimodal AI in high-grade serous ovarian cancer: integrated prediction and clinical decision-making.Frontiers in oncology · 2026Review
- A multimodal framework to identify molecular mechanisms driving patient group-associated morphology through the integration of spatial transcriptomics and whole slide imaging.NPJ artificial intelligence · 2026Article
- Digital pathology and artificial intelligence in breast and gynecologic oncology: from molecular prediction to multimodal integration.Frontiers in oncology · 2026Review
- Artificial intelligence in ovarian cancer prevention and control: a brief review of detection, treatment, and equity (2021-2026).Frontiers in oncology · 2026Review
- Artificial intelligence in ovarian cancer: advancing in precision diagnosis and clinical management.Frontiers in immunology · 2026Review
- Targeting the deubiquitinase USP28 in cancer: navigating context-dependent mechanisms and therapeutic resistance.Frontiers in immunology · 2026Review
- SMMILe enables accurate spatial quantification in digital pathology using multiple-instance learning.Nature cancer · 2025Article
- Development and validation of an artificial intelligence system for triple-negative breast cancer identification and prognosis prediction: a multicentre retrospective study.EClinicalMedicine · 2025Article
Corrections and comments
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Authors and funding
26 authors.
Funding
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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