ArticleNPJ precision oncology2025
Deep learning for predicting prognostic consensus molecular subtypes in cervical cancer from histology images.
Article in NPJ precision oncology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- A unified vision-language model for precision oncology and biomarker prediction in neuroblastoma.Nature communications · 2026Article
- Multimodal integration of pathomics and clinical features improves recurrence risk prediction in pediatric medulloblastoma.Pediatric research · 2026Article
- Reinforcement learning-based WSI diagnostic system for cervical squamous cell carcinoma and precancerous lesions.Journal of translational medicine · 2026Article
- Divergent immune ecosystems in HPV-associated and HPV-independent lower anogenital tract malignancies.Frontiers in immunology · 2026Review
- Multimodal pathomics and clinical features predict postresection permanent hydrocephalus in pediatric medulloblastoma.Journal of neuro-oncology · 2025Article
Corrections and comments
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Authors and funding
7 authors.
Funding
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Abstract
Cervical cancer remains the fourth most common cancer among women worldwide. This study proposes an end-to-end deep learning framework to predict consensus molecular subtypes (CMS) in HPV-positive cervical squamous cell carcinoma (CSCC) from H&E-stained histology slides. Analysing three CSCC cohorts (n = 545), we show our Digital-CMS scores significantly stratify patients by both disease-specific (TCGA p = 0.0022, Oslo p = 0.0495) and disease-free (TCGA p = 0.0495, Oslo p = 0.0282) survival. In addition, our extensive tumour microenvironment analysis reveals differences between the two CMS subtypes, with CMS-C1 tumours exhibit increased lymphocyte presence, while CMS-C2 tumours show high nuclear pleomorphism, elevated neutrophil-to-lymphocyte ratio, and higher malignancy, correlating with poor prognosis. This study introduces a potentially clinically advantageous Digital-CMS score derived from digitised WSIs of routine H&E-stained tissue sections, offers new insights into TME differences impacting patient prognosis and potential therapeutic targets, and identifies histological patterns serving as potential surrogate markers of the CMS subtypes for clinical application.
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Registered trials
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