ArticleAcademic radiology2019
Volumetric Textural Analysis of Colorectal Masses at CT Colonography: Differentiating Benign versus Malignant Pathology and Comparison with Human Reader Performance.
Article in Academic radiology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Construction of a prognostic survival model for colorectal cancer patients using CT image texture analysis: a prospective cohort study.Frontiers in oncology · 2025Article
- Radiomics-based machine learning in the differentiation of benign and malignant bowel wall thickening radiomics in bowel wall thickening.Japanese journal of radiology · 2024Article
- Examining feature extraction and classification modules in machine learning for diagnosis of low-dose computed tomographic screening-detectedJournal of medical imaging (Bellingham, Wash.) · 2024Article
- Article
- A dynamic lesion model for differentiation of malignant and benign pathologies.Scientific reports · 2021Article
- 3D-GLCM CNN: A 3-Dimensional Gray-Level Co-Occurrence Matrix-Based CNN Model for Polyp Classification via CT Colonography.IEEE transactions on medical imaging · 2020Article
- Computed tomography colonography versus colonoscopy for detection of colorectal cancer: a diagnostic performance study.BMC medical imaging · 2020Article
- Multilayer feature selection method for polyp classification via computed tomographic colonography.Journal of medical imaging (Bellingham, Wash.) · 2019Article
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6 authors.
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Abstract
RATIONALE AND
objectivesTo (1) apply a quantitative volumetric textural analysis (VTA) to colorectal masses at CT colonography (CTC) for the differentiation of malignant and benign lesions and to (2) compare VTA with human performance. MATERIALS AND
methodsA validated, quantitative VTA method was applied to 63 pathologically proven colorectal masses (mean size, 4.2 cm; range, 3-8 cm) at noncontrast CTC in 59 adults (mean age, 66.5 years; range, 45.9-91.6 years). Fifty-one percent (32/63) of the masses were invasive adenocarcinoma, and the remaining 49% (31/63) were large benign adenomas. Three readers with CTC experience independently assessed the likelihood of malignancy using a 5-point scale (1 = definitely benign, 2 = probably benign, 3 = indeterminate, 4 = probably malignant, 5 = definitely malignant). Areas under the curve (AUCs) and accuracy levels were compared.
resultsVTA achieved optimal sensitivity of 83.6% vs 91.7% for human readers (P = .034), with specificities of 87.5% and 77.4%, respectively (P = .007). No significant difference in overall accuracy was seen between VTA and human readers (85.5% vs 84.7%, P = .753). The AUC for differentiating benign and malignant lesions was 0.936 for VTA and 0.917 for human readers. Intraclass correlation coefficient among the human readers was 0.76, indicating good to excellent agreement.
conclusionVTA demonstrates excellent performance for distinguishing benign from malignant colorectal masses (≥3 cm) at CTC, comparable yet potentially complementary to experienced human performance.
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