Observational studyScientific reports2021
Deep learning-based virtual cytokeratin staining of gastric carcinomas to measure tumor-stroma ratio.
Observational study in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
14 citing papers in PubMed.
- Review
- Deep learning-driven recognition of panoramic tumor microenvironment features in H&E sections and its application.Journal for immunotherapy of cancer · 2026Review
- Label-free pathological subtyping of non-small cell lung cancer using deep classification and virtual immunohistochemical staining.NPJ digital medicine · 2026Article
- Context-specific image quality assessment for virtual histologic staining: checklist and guideline.Journal of medical imaging (Bellingham, Wash.) · 2026Article
- Automated tumor stroma ratio assessment in colorectal cancer using hybrid deep learning approach.Scientific reports · 2025Article
- E-cadherin expression and tumor-stroma ratio as prognostic biomarkers of peritoneal recurrence in advanced gastric cancer: a digital image analysis-based stratification study.Journal of pathology and translational medicine · 2025Article
- Scalable, trustworthy generative model for virtual multi-staining from H&E whole slide images.PLoS computational biology · 2025Article
- AI-driven 3D bioprinting for regenerative medicine: From bench to bedside.Bioactive materials · 2025Review
- Transparency and Representation in Clinical Research Utilizing Artificial Intelligence in Oncology: A Scoping Review.Cancer medicine · 2025Article
- Deep learning-based virtual H& E staining from label-free autofluorescence lifetime images.Npj imaging · 2024Article
- Tumor-Stroma Ratio in Colorectal Cancer-Comparison between Human Estimation and Automated Assessment.Cancers · 2023Article
- Deep learning-enabled virtual histological staining of biological samples.Light, science & applications · 2023Review
- Digital staining facilitates biomedical microscopy.Frontiers in bioinformatics · 2023Article
- Preliminary study of some of the main intratumor stroma components in gastric carcinomas.Romanian journal of morphology and embryology = Revue roumaine de morphologie et embryologieArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The tumor-stroma ratio (TSR) determined by pathologists is subject to intra- and inter-observer variability. We aimed to develop a computational quantification method of TSR using deep learning-based virtual cytokeratin staining algorithms. Patients with 373 advanced (stage III [n = 171] and IV [n = 202]) gastric cancers were analyzed for TSR. Moderate agreement was observed, with a kappa value of 0.623, between deep learning metrics (dTSR) and visual measurement by pathologists (vTSR) and the area under the curve of receiver operating characteristic of 0.907. Moreover, dTSR was significantly associated with the overall survival of the patients (P = 0.0024). In conclusion, we developed a virtual cytokeratin staining and deep learning-based TSR measurement, which may aid in the diagnosis of TSR in gastric cancer.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.