ArticleJournal of translational medicine2020
Glandular orientation and shape determined by computational pathology could identify aggressive tumor for early colon carcinoma: a triple-center study.
Article in Journal of translational medicine, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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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.
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Who cites it
4 citing papers in PubMed, 6 citations in OpenAlex.
- Review
- Computational pathology: A survey review and the way forward.Journal of pathology informatics · 2024Review
- Interpretable prediction of 30-day mortality in patients with acute pancreatitis based on machine learning and SHAP.BMC medical informatics and decision making · 2024Article
- Machine learning model identifies aggressive acute pancreatitis within 48 h of admission: a large retrospective study.BMC medical informatics and decision making · 2022Article
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Authors and funding
11 authors at 4 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundIdentifying the early-stage colon adenocarcinoma (ECA) patients who have lower risk cancer vs. the higher risk cancer could improve disease prognosis. Our study aimed to explore whether the glandular morphological features determined by computational pathology could identify high risk cancer in ECA via H&E images digitally.
methods532 ECA patients retrospectively from 2 independent data centers, as well as 113 from The Cancer Genome Atlas (TCGA), were enrolled in this study. Four tissue microarrays (TMAs) were constructed across ECA hematoxylin and eosin (H&E) stained slides. 797 quantitative glandular morphometric features were extracted and 5 most prognostic features were identified using minimum redundancy maximum relevance to construct an image classifier. The image classifier was evaluated on D2/D3 = 223, D4 = 46, D5 = 113. The expression of Ki67 and serum CEA levels were scored on D3, aiming to explore the correlations between image classifier and immunohistochemistry data and serum CEA levels. The roles of clinicopathological data and ECAHBC were evaluated by univariate and multivariate analyses for prognostic value.
resultsThe image classifier could predict ECA recurrence (accuracy of 88.1%). ECA histomorphometric-based image classifier (ECAHBC) was an independent prognostic factor for poorer disease-specific survival [DSS, (HR = 9.65, 95% CI 2.15-43.12, P = 0.003)]. Significant correlations were observed between ECAHBC-positive patients and positivity of Ki67 labeling index (Ki67Li) and serum CEA.
conclusionGlandular orientation and shape could predict the high risk cancer in ECA and contribute to precision oncology. Computational pathology is emerging as a viable and objective means of identifying predictive biomarkers for cancer patients.
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