Literature DB >> 18562741

Performance of a previously validated CT colonography computer-aided detection system in a new patient population.

Ronald M Summers1, Laurie R Handwerker, Perry J Pickhardt, Robert L Van Uitert, Keshav K Deshpande, Srinath Yeshwant, Jianhua Yao, Marek Franaszek.   

Abstract

OBJECTIVE: A computer-aided detection (CAD) system with high sensitivity in the detection of adenomatous polyps in varied CT colonography (CTC) data sets increases the utility of CAD in the clinical setting. The purpose of this study was to evaluate the standalone performance of an existing CAD system with a new set of CTC data from screening patients at an institution and geographic location different from those at which the CAD system was trained.
MATERIALS AND METHODS: CTC data were collected from the records of 104 patients undergoing screening for colorectal neoplasia. Most of the patients were at average risk, had CTC findings suggestive of polyps, and underwent colonoscopy. Patients underwent cathartic bowel preparation, were given an oral contrast agent, and underwent imaging in the prone and supine positions. The patients had 86 adenomas confirmed at same-day optical colonoscopy; 47 of these tumors were 10 mm in diameter or larger, and 39 measured 6-9 mm. The CTC data were analyzed with an existing CAD system for colonography that was trained with previously acquired data. In a previous non-polyp-enriched screening cohort, the standalone performance of the CAD system was 93.3% (28/30) sensitivity for adenomatous polyps 10 mm or larger, 51.1% (47/92) sensitivity for adenomas 6-9 mm, and a mean false-positive rate of 8.6 per patient. Sensitivity comparisons were made with findings in the previous study.
RESULTS: The CAD system had per-polyp sensitivities of 91.5% (43/47; 95% CI, 78.7-97.2%; p = 1.0) for adenomas 10 mm or larger and 82.1% (32/39; 65.9-91.9%; p = 0.0009) for adenomas 6-9 mm. The per-patient sensitivities were 97.6% (40/41; 85.6-99.9%; p = 0.6) for patients with adenomas 10 mm or larger and 82.4% (28/34; 64.8-92.6%; p = 0.047) for patients with adenomas 6-9 mm. The mean and median false-positive rates were 9.6 +/- 9.6 and 7.0 per patient, respectively. Common reasons for CAD misses (false-negative findings) were the presence of adherent contrast medium, flat adenomas, and adenomas located on or adjacent to normal colonic folds. In a random sample, 72.5% (29/40) of false-positive findings were attributable to folds or residual feces.
CONCLUSION: The CAD system evaluated has a high level of performance in the detection of adenomatous polyps with CTC data from a polyp-enriched cohort different from that used to train the system.

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Year:  2008        PMID: 18562741     DOI: 10.2214/AJR.07.3354

Source DB:  PubMed          Journal:  AJR Am J Roentgenol        ISSN: 0361-803X            Impact factor:   3.959


  10 in total

Review 1.  Improving the accuracy of CTC interpretation: computer-aided detection.

Authors:  Ronald M Summers
Journal:  Gastrointest Endosc Clin N Am       Date:  2010-04

2.  Evaluation of computer-aided detection and diagnosis systems.

Authors:  Nicholas Petrick; Berkman Sahiner; Samuel G Armato; Alberto Bert; Loredana Correale; Silvia Delsanto; Matthew T Freedman; David Fryd; David Gur; Lubomir Hadjiiski; Zhimin Huo; Yulei Jiang; Lia Morra; Sophie Paquerault; Vikas Raykar; Frank Samuelson; Ronald M Summers; Georgia Tourassi; Hiroyuki Yoshida; Bin Zheng; Chuan Zhou; Heang-Ping Chan
Journal:  Med Phys       Date:  2013-08       Impact factor: 4.071

3.  Breast US computer-aided diagnosis system: robustness across urban populations in South Korea and the United States.

Authors:  Nicholas P Gruszauskas; Karen Drukker; Maryellen L Giger; Ruey-Feng Chang; Charlene A Sennett; Woo Kyung Moon; Lorenzo L Pesce
Journal:  Radiology       Date:  2009-10-28       Impact factor: 11.105

4.  Improved computer-aided detection of small polyps in CT colonography using interpolation for curvature estimation.

Authors:  Jiamin Liu; Suraj Kabadi; Robert Van Uitert; Nicholas Petrick; Rachid Deriche; Ronald M Summers
Journal:  Med Phys       Date:  2011-07       Impact factor: 4.071

Review 5.  Progress in Fully Automated Abdominal CT Interpretation.

Authors:  Ronald M Summers
Journal:  AJR Am J Roentgenol       Date:  2016-04-21       Impact factor: 3.959

6.  CT colonography: computer-assisted detection of colorectal cancer.

Authors:  C Robinson; S Halligan; G Iinuma; W Topping; S Punwani; L Honeyfield; S A Taylor
Journal:  Br J Radiol       Date:  2010-11-16       Impact factor: 3.039

7.  Comparison of calibrated and uncalibrated bone mineral density by CT to DEXA in menopausal women.

Authors:  Y Miyabara; D Holmes; J Camp; V M Miller; A E Kearns
Journal:  Climacteric       Date:  2011-12-17       Impact factor: 3.005

8.  Feasibility of using the marginal blood vessels as reference landmarks for CT colonography.

Authors:  Zhuoshi Wei; Jianhua Yao; Shijun Wang; Jiamin Liu; Andrew J Dwyer; Perry J Pickhardt; Wieslaw L Nowinski; Ronald M Summers
Journal:  AJR Am J Roentgenol       Date:  2014-01       Impact factor: 3.959

9.  Preparing Medical Imaging Data for Machine Learning.

Authors:  Martin J Willemink; Wojciech A Koszek; Cailin Hardell; Jie Wu; Dominik Fleischmann; Hugh Harvey; Les R Folio; Ronald M Summers; Daniel L Rubin; Matthew P Lungren
Journal:  Radiology       Date:  2020-02-18       Impact factor: 11.105

Review 10.  Development of artificial intelligence technology in diagnosis, treatment, and prognosis of colorectal cancer.

Authors:  Feng Liang; Shu Wang; Kai Zhang; Tong-Jun Liu; Jian-Nan Li
Journal:  World J Gastrointest Oncol       Date:  2022-01-15
  10 in total

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