ArticleGastroenterology2022
Quantitative Pathologic Analysis of Digitized Images of Colorectal Carcinoma Improves Prediction of Recurrence-Free Survival.
Article in Gastroenterology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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Who cites it
19 citing papers in PubMed.
- Article
- TIMEL: Deep learning-statistical integration reveals spatial stromal and immune signatures of aggressive colon cancer.Journal of pathology informatics · 2026Article
- [Artificial intelligence-assisted diagnostics in the pathology of internal and oncological diseases].Innere Medizin (Heidelberg, Germany) · 2026Review
- Utility of quantitative pathologic analysis of pT1 colorectal carcinomas to improve prediction of lymph node metastasis.Virchows Archiv : an international journal of pathology · 2026Article
- Article
- Assessment of Tumor Margin and Heterogeneity of Colorectal Cancer Using Imaging Mass Spectrometry and Image Segmentation.Cancers · 2026Article
- Foundation models in computational pathology: methods, applications and clinical implications.BMJ oncology · 2026Review
- Deficient mismatch repair/microsatellite instability-high colorectal cancer: current treatment paradigms, limitations and future perspectives.BMJ oncology · 2026Review
- Deep learning-enabled multiphoton microscopy predicts colorectal cancer recurrence from routine FFPE specimens.NPJ digital medicine · 2025Article
- Nerve growth factor: what can surgeons and oncologists learn from a neurological and psychological biomarker?Molecular medicine (Cambridge, Mass.) · 2025Review
- Comparing non-machine learning vs. machine learning methods for Ki67 scoring in gastrointestinal neuroendocrine tumors.Scientific reports · 2025Article
- Claudin 18.2 Expression in 1404 Digestive Tract Adenocarcinomas Including 1175 Colorectal Carcinomas: Distinct Colorectal Carcinoma Subtypes Are Claudin 18.2 Positive.Modern pathology : an official journal of the United States and Canadian Academy of Pathology, Inc · 2025Article
- Automated deep learning-based assessment of tumour-infiltrating lymphocyte density determines prognosis in colorectal cancer.Journal of translational medicine · 2025Article
- Predictors of Local Invasion in Infiltrative Basal Cell Carcinoma: Tumour Budding Outperforms the WHO Subtyping.Acta dermato-venereologica · 2024Article
- Prediction of recurrence risk in endometrial cancer with multimodal deep learning.Nature medicine · 2024Article
- Use of Deep Learning to Evaluate Tumor Microenvironmental Features for Prediction of Colon Cancer Recurrence.Cancer research communications · 2024Article
- Improved Risk-Stratification Scheme for Mismatch-Repair Proficient Stage II Colorectal Cancers Using the Digital Pathology Biomarker QuantCRC.Clinical cancer research : an official journal of the American Association for Cancer Research · 2024Article
- Article
- Desmoplastic Reaction Associates with Prognosis and Adjuvant Chemotherapy Response in Colorectal Cancer: A Multicenter Retrospective Study.Cancer research communications · 2023Article
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25 authors.
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Abstract
BACKGROUND &
aimsTo examine whether quantitative pathologic analysis of digitized hematoxylin and eosin slides of colorectal carcinoma (CRC) correlates with clinicopathologic features, molecular alterations, and prognosis.
methodsA quantitative segmentation algorithm (QuantCRC) was applied to 6468 digitized hematoxylin and eosin slides of CRCs. Fifteen parameters were recorded from each image and tested for associations with clinicopathologic features and molecular alterations. A prognostic model was developed to predict recurrence-free survival using data from the internal cohort (n = 1928) and validated on an internal test (n = 483) and external cohort (n = 938).
resultsThere were significant differences in QuantCRC according to stage, histologic subtype, grade, venous/lymphatic/perineural invasion, tumor budding, CD8 immunohistochemistry, mismatch repair status, KRAS mutation, BRAF mutation, and CpG methylation. A prognostic model incorporating stage, mismatch repair, and QuantCRC resulted in a Harrell's concordance (c)-index of 0.714 (95% confidence interval [CI], 0.702-0.724) in the internal test and 0.744 (95% CI, 0.741-0.754) in the external cohort. Removing QuantCRC from the model reduced the c-index to 0.679 (95% CI, 0.673-0.694) in the external cohort. Prognostic risk groups were identified, which provided a hazard ratio of 2.24 (95% CI, 1.33-3.87, P = .004) for low vs high-risk stage III CRCs and 2.36 (95% CI, 1.07-5.20, P = .03) for low vs high-risk stage II CRCs, in the external cohort after adjusting for established risk factors. The predicted median 36-month recurrence rate for high-risk stage III CRCs was 32.7% vs 13.4% for low-risk stage III and 15.8% for high-risk stage II vs 5.4% for low-risk stage II CRCs.
conclusionsQuantCRC provides a powerful adjunct to routine pathologic reporting of CRC. A prognostic model using QuantCRC improves prediction of recurrence-free survival.
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