PURPOSE: To assess the effect of using computer-aided detection (CAD) in second-read mode on readers' accuracy in interpreting computed tomographic (CT) colonographic images. MATERIALS AND METHODS: The contributing institutions performed the examinations under approval of their local institutional review board, with waiver of informed consent, for this HIPAA-compliant study. A cohort of 100 colonoscopy-proved cases was used: In 52 patients with findings positive for polyps, 74 polyps of 6 mm or larger were observed in 65 colonic segments; in 48 patients with findings negative for polyps, no polyps were found. Nineteen blinded readers interpreted each case at two different times, with and without the assistance of a commercial CAD system. The effect of CAD was assessed in segment-level and patient-level receiver operating characteristic (ROC) curve analyses. RESULTS: Thirteen (68%) of 19 readers demonstrated higher accuracy with CAD, as measured with the segment-level area under the ROC curve (AUC). The readers' average segment-level AUC with CAD (0.758) was significantly greater (P = .015) than the average AUC in the unassisted read (0.737). Readers' per-segment, per-patient, and per-polyp sensitivity for all polyps of 6 mm or larger was higher (P < .011, .007, .005, respectively) for readings with CAD compared with unassisted readings (0.517 versus 0.465, 0.521 versus 0.466, and 0.477 versus 0.422, respectively). Sensitivity for patients with at least one large polyp of 10 mm or larger was also higher (P < .047) with CAD than without (0.777 versus 0.743). Average reader sensitivity also improved with CAD by more than 0.08 for small adenomas. Use of CAD reduced specificity of readers by 0.025 (P = .05). CONCLUSION: Use of CAD resulted in a significant improvement in overall reader performance. CAD improves reader sensitivity when measured per segment, per patient, and per polyp for small polyps and adenomas and also reduces specificity by a small amount. (c) RSNA, 2010.
PURPOSE: To assess the effect of using computer-aided detection (CAD) in second-read mode on readers' accuracy in interpreting computed tomographic (CT) colonographic images. MATERIALS AND METHODS: The contributing institutions performed the examinations under approval of their local institutional review board, with waiver of informed consent, for this HIPAA-compliant study. A cohort of 100 colonoscopy-proved cases was used: In 52 patients with findings positive for polyps, 74 polyps of 6 mm or larger were observed in 65 colonic segments; in 48 patients with findings negative for polyps, no polyps were found. Nineteen blinded readers interpreted each case at two different times, with and without the assistance of a commercial CAD system. The effect of CAD was assessed in segment-level and patient-level receiver operating characteristic (ROC) curve analyses. RESULTS: Thirteen (68%) of 19 readers demonstrated higher accuracy with CAD, as measured with the segment-level area under the ROC curve (AUC). The readers' average segment-level AUC with CAD (0.758) was significantly greater (P = .015) than the average AUC in the unassisted read (0.737). Readers' per-segment, per-patient, and per-polyp sensitivity for all polyps of 6 mm or larger was higher (P < .011, .007, .005, respectively) for readings with CAD compared with unassisted readings (0.517 versus 0.465, 0.521 versus 0.466, and 0.477 versus 0.422, respectively). Sensitivity for patients with at least one large polyp of 10 mm or larger was also higher (P < .047) with CAD than without (0.777 versus 0.743). Average reader sensitivity also improved with CAD by more than 0.08 for small adenomas. Use of CAD reduced specificity of readers by 0.025 (P = .05). CONCLUSION: Use of CAD resulted in a significant improvement in overall reader performance. CAD improves reader sensitivity when measured per segment, per patient, and per polyp for small polyps and adenomas and also reduces specificity by a small amount. (c) RSNA, 2010.
Authors: Perry J Pickhardt; J Richard Choi; Inku Hwang; James A Butler; Michael L Puckett; Hans A Hildebrandt; Roy K Wong; Pamela A Nugent; Pauline A Mysliwiec; William R Schindler Journal: N Engl J Med Date: 2003-12-01 Impact factor: 91.245
Authors: Aravind Mani; Sandy Napel; David S Paik; R Brooke Jeffrey; Judy Yee; Eric W Olcott; Rupert Prokesch; Marta Davila; Pamela Schraedley-Desmond; Christopher F Beaulieu Journal: J Comput Assist Tomogr Date: 2004 May-Jun Impact factor: 1.826
Authors: L Bogoni; P Cathier; M Dundar; A Jerebko; S Lakare; J Liang; S Periaswamy; M E Baker; M Macari Journal: Br J Radiol Date: 2005 Impact factor: 3.039
Authors: Perry J Pickhardt; Pamela A Nugent; Pauline A Mysliwiec; J Richard Choi; William R Schindler Journal: Ann Intern Med Date: 2004-09-07 Impact factor: 25.391
Authors: Abraham H Dachman; Katherine B Kelly; Michael P Zintsmaster; Rich Rana; Shweta Khankari; Joseph D Novak; Arif N Ali; Adnan Qalbani; Joel G Fletcher Journal: Radiology Date: 2008-10 Impact factor: 11.105
Authors: Tan B Nguyen; Shijun Wang; Vishal Anugu; Natalie Rose; Matthew McKenna; Nicholas Petrick; Joseph E Burns; Ronald M Summers Journal: Radiology Date: 2012-01-24 Impact factor: 11.105
Authors: Igor Trilisky; Kristen Wroblewski; Michael W Vannier; John M Horne; Abraham H Dachman Journal: Radiographics Date: 2014 Nov-Dec Impact factor: 5.333
Authors: Thomas Mang; Luca Bogoni; Vikram X Anand; Dass Chandra; Andrew J Curtin; Anna S Lev-Toaff; Gerardo Hermosillo; Ralph Noah; Vikas Raykar; Marcos Salganicoff; Robert Shaw; Susan Summerton; Rafel F R Tappouni; Helmut Ringel; Michael Weber; Matthias Wolf; Nancy A Obuchowski Journal: Eur Radiol Date: 2014-05-10 Impact factor: 5.315
Authors: Joseph E Burns; Jianhua Yao; Tatjana S Wiese; Hector E Muñoz; Elizabeth C Jones; Ronald M Summers Journal: Radiology Date: 2013-02-28 Impact factor: 11.105