ArticleCellular oncology (Dordrecht, Netherlands)2019
Computer aided quantification of intratumoral stroma yields an independent prognosticator in rectal cancer.
Article in Cellular oncology (Dordrecht, Netherlands), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 64 papers, 4 of them syntheses that pooled it.
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Who cites it
64 citing papers in PubMed, 4 syntheses or guidelines pooled it, 124 citations in OpenAlex.
- A scoping review of TSR analysis in colorectal cancer: implications for automated solutions.Oncology reviews · 2025Pooled it
- Significance of Tumor-Stroma Ratio (TSR) in Predicting Outcomes of Malignant Tumors.Medicina (Kaunas, Lithuania) · 2023Pooled it
- Impact of Tumor-Stroma Ratio on the Prognosis of Colorectal Cancer: A Systematic Review.Frontiers in oncology · 2021Pooled it
- Prognostic Value of Tumor-Stroma Ratio in Rectal Cancer: A Systematic Review and Meta-analysis.Frontiers in oncology · 2021Pooled it
- Review
- Added value of the tumor-stroma ratio to the PREDICT model for breast cancer.Breast (Edinburgh, Scotland) · 2026Article
- Artificial intelligence in digital pathology diagnosis and analysis: technologies, challenges, and future prospects.Military Medical Research · 2026Review
- Automating tumor-stroma ratio quantification in colon cancer patients from the UNITED study.ESMO open · 2026Article
- Automated tumor stroma ratio assessment in colorectal cancer using hybrid deep learning approach.Scientific reports · 2025Article
- AI-based tumor-stroma ratio quantification algorithm: comprehensive evaluation of prognostic role in primary colorectal cancer.Virchows Archiv : an international journal of pathology · 2025Article
- The tumour-stroma ratio as predictive aid towards a biopsy-based treatment strategy in rectal carcinoma.Histopathology · 2025Article
- Tumor‑stroma ratio as a clinical prognostic factor in colorectal carcinoma: A meta‑analysis of 7,934 patients.Oncology letters · 2025Article
- Mathematical modelling and deep learning algorithms to automate assessment of single and digitally multiplexed immunohistochemical stains in tumoural stroma.Journal of pathology informatics · 2024Article
- Deep learning application in prediction of cancer molecular alterations based on pathological images: a bibliographic analysis via CiteSpace.Journal of cancer research and clinical oncology · 2024Article
- The Glasgow Microenvironment Score: an exemplar of contemporary biomarker evolution in colorectal cancer.The journal of pathology. Clinical research · 2024Review
- Artificial intelligence to unlock real-world evidence in clinical oncology: A primer on recent advances.Cancer medicine · 2024Review
- Deep learning based digital pathology for predicting treatment response to first-line PD-1 blockade in advanced gastric cancer.Journal of translational medicine · 2024Article
- Predicting 5-year recurrence risk in colorectal cancer: development and validation of a histology-based deep learning approach.British journal of cancer · 2024Article
- Review
- Automatic quantification of tumor-stroma ratio as a prognostic marker for pancreatic cancer.PloS one · 2024Article
4 more citing papers are in PubMed but not listed here.
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Authors and funding
10 authors at 5 institutions in 1 country.
Funding
Abstract
purposeTumor-stroma ratio (TSR) serves as an independent prognostic factor in colorectal cancer and other solid malignancies. The recent introduction of digital pathology in routine tissue diagnostics holds opportunities for automated TSR analysis. We investigated the potential of computer-aided quantification of intratumoral stroma in rectal cancer whole-slide images.
methodsHistological slides from 129 rectal adenocarcinoma patients were analyzed by two experts who selected a suitable stroma hot-spot and visually assessed TSR. A semi-automatic method based on deep learning was trained to segment all relevant tissue types in rectal cancer histology and subsequently applied to the hot-spots provided by the experts. Patients were assigned to a 'stroma-high' or 'stroma-low' group by both TSR methods (visual and automated). This allowed for prognostic comparison between the two methods in terms of disease-specific and disease-free survival times.
resultsWith stroma-low as baseline, automated TSR was found to be prognostic independent of age, gender, pT-stage, lymph node status, tumor grade, and whether adjuvant therapy was given, both for disease-specific survival (hazard ratio = 2.48 (95% confidence interval 1.29-4.78)) and for disease-free survival (hazard ratio = 2.05 (95% confidence interval 1.11-3.78)). Visually assessed TSR did not serve as an independent prognostic factor in multivariate analysis.
conclusionsThis work shows that TSR is an independent prognosticator in rectal cancer when assessed automatically in user-provided stroma hot-spots. The deep learning-based technology presented here may be a significant aid to pathologists in routine diagnostics.
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