Deep learning for predicting prognostic consensus molecular subtypes in cervical cancer from histology images

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 coh...

Full description

Saved in:
Bibliographic Details
Published in:NPJ precision oncology 2025-01, Vol.9 (1), p.11-13, Article 11
Main Authors: Wang, Ruoyu, Gunesli, Gozde N., Skingen, Vilde Eide, Valen, Kari-Anne Frikstad, Lyng, Heidi, Young, Lawrence S., Rajpoot, Nasir
Format: Article
Language:English
Subjects:
631/114
631/67/1517/1371
631/67/1857
631/67/2321
631/67/327
Cancer Research
Cancer therapies
Cervical cancer
Clinical trials
Deep learning
Disease prevention
DNA methylation
Epigenetics
Gene Therapy
Histology
Human Genetics
Human papillomavirus
Internal Medicine
Keratin
Medical prognosis
Medicine
Medicine & Public Health
Oncology
Patients
Squamous cell carcinoma
Survival analysis
Tumors
Womens health
Citations: Items that this one cites
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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.
ISSN:2397-768X
2397-768X
DOI:10.1038/s41698-024-00778-5