Back to Search
Start Over
From pixels to patient care: deep learning-enabled pathomics signature offers precise outcome predictions for immunotherapy in esophageal squamous cell cancer
- Source :
- Journal of Translational Medicine, Vol 22, Iss 1, Pp 1-12 (2024)
- Publication Year :
- 2024
- Publisher :
- BMC, 2024.
-
Abstract
- Abstract Background Immunotherapy has significantly improved survival of esophageal squamous cell cancer (ESCC) patients, however the clinical benefit was limited to only a small portion of patients. This study aimed to perform a deep learning signature based on H&E-stained pathological specimens to accurately predict the clinical benefit of PD-1 inhibitors in ESCC patients. Methods ESCC patients receiving PD-1 inhibitors from Shandong Cancer Hospital were included. WSI images of H&E-stained histological specimens of included patients were collected, and randomly divided into training (70%) and validation (30%) sets. The labels of images were defined by the progression-free survival (PFS) with the interval of 4 months. The pretrained ViT model was used for patch-level model training, and all patches were projected into probabilities after linear classifier. Then the most predictive patches were passed to RNN for final patient-level prediction to construct ESCC-pathomics signature (ESCC-PS). Accuracy rate and survival analysis were performed to evaluate the performance of ViT-RNN survival model in validation cohort. Results 163 ESCC patients receiving PD-1 inhibitors were included for model training. There were 486,188 patches of 1024*1024 pixels from 324 WSI images of H&E-stained histological specimens after image pre-processing. There were 120 patients with 227 images in training cohort and 43 patients with 97 images in validation cohort, with balanced baseline characteristics between two groups. The ESCC-PS achieved an accuracy of 84.5% in the validation cohort, and could distinguish patients into three risk groups with the median PFS of 2.6, 4.5 and 12.9 months (P
- Subjects :
- Pathomics
Esophageal cancer
Immunotherapy
Deep learning
Survival
Medicine
Subjects
Details
- Language :
- English
- ISSN :
- 14795876
- Volume :
- 22
- Issue :
- 1
- Database :
- Directory of Open Access Journals
- Journal :
- Journal of Translational Medicine
- Publication Type :
- Academic Journal
- Accession number :
- edsdoj.fee00fb69b364a43a7837a917b6b820c
- Document Type :
- article
- Full Text :
- https://doi.org/10.1186/s12967-024-04997-z