ArticleThe Journal of pathology2022
Computer-extracted features of nuclear morphology in hematoxylin and eosin images distinguish stage II and IV colon tumors.
Article in The Journal of pathology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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9 citing papers in PubMed.
- Quantitative Histomorphometric Analysis of Collagen Bundles in Masson's Trichrome Stained Rat (Health science reports · 2026Article
- Two decades of artificial intelligence in colon cancer diagnosis and treatment: a bibliometric analysis of research trends (2003-2023).Updates in surgery · 2026Review
- Interpretable multimodal radiopathomics model predicting pathological complete response to neoadjuvant chemoimmunotherapy in esophageal squamous cell carcinoma.Journal for immunotherapy of cancer · 2025Article
- Aligning computational pathology with clinical practice for colorectal cancer.NPJ precision oncology · 2025Review
- Development of a preoperative predictive classifier and tailored staging system for obstructive colorectal cancer.BMC surgery · 2025Article
- Predicting ustekinumab treatment response in Crohn's disease using pre-treatment biopsy images.Bioinformatics (Oxford, England) · 2025Article
- AI powered quantification of nuclear morphology in cancers enables prediction of genome instability and prognosis.NPJ precision oncology · 2024Article
- Artificial intelligence and machine learning applications for cultured meat.Frontiers in artificial intelligence · 2024Review
- Stain normalization in digital pathology: Clinical multi-center evaluation of image quality.Journal of pathology informatics · 2022Article
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7 authors.
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Abstract
We assessed the utility of quantitative features of colon cancer nuclei, extracted from digitized hematoxylin and eosin-stained whole slide images (WSIs), to distinguish between stage II and stage IV colon cancers. Our discovery cohort comprised 100 stage II and stage IV colon cancer cases sourced from the University Hospitals Cleveland Medical Center (UHCMC). We performed initial (independent) model validation on 51 (143) stage II and 79 (54) stage IV colon cancer cases from UHCMC (The Cancer Genome Atlas's Colon Adenocarcinoma, TCGA-COAD, cohort). Our approach comprised the following steps: (1) a fully convolutional deep neural network with VGG-18 architecture was trained to locate cancer on WSIs; (2) another deep-learning model based on Mask-RCNN with Resnet-50 architecture was used to segment all nuclei from within the identified cancer region; (3) a total of 26 641 quantitative morphometric features pertaining to nuclear shape, size, and texture were extracted from within and outside tumor nuclei; (4) a random forest classifier was trained to distinguish between stage II and stage IV colon cancers using the five most discriminatory features selected by the Wilcoxon rank-sum test. Our trained classifier using these top five features yielded an AUC of 0.81 and 0.78, respectively, on the held-out cases in the UHCMC and TCGA validation sets. For 197 TCGA-COAD cases, the Cox proportional hazards model yielded a hazard ratio of 2.20 (95% CI 1.24-3.88) with a concordance index of 0.71, using only the top five features for risk stratification of overall survival. The Kaplan-Meier estimate also showed statistically significant separation between the low-risk and high-risk patients, with a log-rank P value of 0.0097. Finally, unsupervised clustering of the top five features revealed that stage IV colon cancers with peritoneal spread were morphologically more similar to stage II colon cancers with no long-term metastases than to stage IV colon cancers with hematogenous spread. © 2022 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.
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