B Dustin Pooler1, Meghan G Lubner2, Jake R Theis2, Richard B Halberg3, Zhengrong Liang4, Perry J Pickhardt2. 1. Departments of Radiology, University of Wisconsin School of Medicine and Public Health, E3/311 Clinical Science Center, 600 Highland Ave., Madison, WI 53792-3252. Electronic address: bpooler@uwhealth.org. 2. Departments of Radiology, University of Wisconsin School of Medicine and Public Health, E3/311 Clinical Science Center, 600 Highland Ave., Madison, WI 53792-3252. 3. Departments of Gastroenterology & Hepatology, University of Wisconsin School of Medicine and Public Health, Madison, WI. 4. Departments of Radiology, State University of New York, Stony Brook, NY; Departments of Computer Science, State University of New York, Stony Brook, NY; Departments of Biomedical Engineering, State University of New York, Stony Brook, NY.
Abstract
RATIONALE AND OBJECTIVES: To (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 METHODS: A 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. RESULTS: VTA 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. CONCLUSION: VTA demonstrates excellent performance for distinguishing benign from malignant colorectal masses (≥3 cm) at CTC, comparable yet potentially complementary to experienced human performance.
RATIONALE AND OBJECTIVES: To (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 METHODS: A 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. RESULTS: VTA 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. CONCLUSION: VTA demonstrates excellent performance for distinguishing benign from malignant colorectal masses (≥3 cm) at CTC, comparable yet potentially complementary to experienced human performance.
Authors: Weiguo Cao; Zhengrong Liang; Marc J Pomeroy; Kenneth Ng; Shu Zhang; Yongfeng Gao; Perry J Pickhardt; Matthew A Barish; Almas F Abbasi; Hongbing Lu Journal: J Med Imaging (Bellingham) Date: 2019-12-27
Authors: Jiaxing Tan; Yongfeng Gao; Zhengrong Liang; Weiguo Cao; Marc J Pomeroy; Yumei Huo; Lihong Li; Matthew A Barish; Almas F Abbasi; Perry J Pickhardt Journal: IEEE Trans Med Imaging Date: 2019-12-30 Impact factor: 10.048