Literature DB >> 17885187

Computer-aided detection of colorectal polyps: can it improve sensitivity of less-experienced readers? Preliminary findings.

Mark E Baker1, Luca Bogoni, Nancy A Obuchowski, Chandra Dass, Renee M Kendzierski, Erick M Remer, David M Einstein, Pascal Cathier, Anna Jerebko, Sarang Lakare, Andrew Blum, Dina F Caroline, Michael Macari.   

Abstract

PURPOSE: To determine whether computer-aided detection (CAD) applied to computed tomographic (CT) colonography can help improve sensitivity of polyp detection by less-experienced radiologist readers, with colonoscopy or consensus used as the reference standard.
MATERIALS AND METHODS: The release of the CT colonographic studies was approved by the individual institutional review boards of each institution. Institutions from the United States were HIPAA compliant. Written informed consent was waived at all institutions. The CT colonographic studies in 30 patients from six institutions were collected; 24 images depicted at least one confirmed polyp 6 mm or larger (39 total polyps) and six depicted no polyps. By using an investigational software package, seven less-experienced readers from two institutions evaluated the CT colonographic images and marked or scored polyps by using a five-point scale before and after CAD. The time needed to interpret the CT colonographic findings without CAD and then to re-evaluate them with CAD was recorded. For each reader, the McNemar test, adjusted for clustered data, was used to compare sensitivities for readers without and with CAD; a Wilcoxon signed-rank test was used to analyze the number of false-positive results per patient.
RESULTS: The average sensitivity of the seven readers for polyp detection was significantly improved with CAD-from 0.810 to 0.908 (P=.0152). The number of false-positive results per patient without and with CAD increased from 0.70 to 0.96 (95% confidence interval for the increase: -0.39, 0.91). The mean total time for the readings was 17 minutes 54 seconds; for interpretation of CT colonographic findings alone, the mean time was 14 minutes 16 seconds; and for review of CAD findings, the mean time was 3 minutes 38 seconds.
CONCLUSION: Results of this feasibility study suggest that CAD for CT colonography significantly improves per-polyp detection for less-experienced readers. Copyright (c) RSNA, 2007.

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Mesh:

Year:  2007        PMID: 17885187     DOI: 10.1148/radiol.2451061116

Source DB:  PubMed          Journal:  Radiology        ISSN: 0033-8419            Impact factor:   11.105


  25 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.  Computer-based self-training for CT colonography with and without CAD.

Authors:  Lapo Sali; Silvia Delsanto; Daniela Sacchetto; Loredana Correale; Massimo Falchini; Andrea Ferraris; Giovanni Gandini; Giulia Grazzini; Franco Iafrate; Gabriella Iussich; Lia Morra; Andrea Laghi; Mario Mascalchi; Daniele Regge
Journal:  Eur Radiol       Date:  2018-05-23       Impact factor: 5.315

3.  CT colonography: advanced computer-aided detection scheme utilizing MTANNs for detection of "missed" polyps in a multicenter clinical trial.

Authors:  Kenji Suzuki; Don C Rockey; Abraham H Dachman
Journal:  Med Phys       Date:  2010-01       Impact factor: 4.071

4.  Computer-aided detection of pulmonary embolism at CT pulmonary angiography: can it improve performance of inexperienced readers?

Authors:  Kevin N Blackmon; Charles Florin; Luca Bogoni; Joshua W McCain; James D Koonce; Heon Lee; Gorka Bastarrika; Christian Thilo; Philip Costello; Marcos Salganicoff; U Joseph Schoepf
Journal:  Eur Radiol       Date:  2011-01-13       Impact factor: 5.315

5.  A computer-aided algorithm to quantitatively predict lymph node status on MRI in rectal cancer.

Authors:  D M L Tse; N Joshi; E M Anderson; M Brady; F V Gleeson
Journal:  Br J Radiol       Date:  2012-09       Impact factor: 3.039

6.  Computer-aided stenosis detection at coronary CT angiography: effect on performance of readers with different experience levels.

Authors:  Christian Thilo; Mulugeta Gebregziabher; Felix G Meinel; Roman Goldenberg; John W Nance; Elisabeth M Arnoldi; Lashonda D Soma; Ullrich Ebersberger; Philip Blanke; Richard L Coursey; Michael A Rosenblum; Peter L Zwerner; U Joseph Schoepf
Journal:  Eur Radiol       Date:  2014-10-15       Impact factor: 5.315

7.  Computer-aided diagnosis for detection of lacunar infarcts on MR images: ROC analysis of radiologists' performance.

Authors:  Yoshikazu Uchiyama; Takahiko Asano; Hiroki Kato; Takeshi Hara; Masayuki Kanematsu; Hiroaki Hoshi; Toru Iwama; Hiroshi Fujita
Journal:  J Digit Imaging       Date:  2012-08       Impact factor: 4.056

8.  CT colonography computer-aided polyp detection: Effect on radiologist observers of polyp identification by CAD on both the supine and prone scans.

Authors:  Ronald M Summers; Jiamin Liu; Bhavya Rehani; Phillip Stafford; Linda Brown; Adeline Louie; Duncan S Barlow; Donald W Jensen; Brooks Cash; J Richard Choi; Perry J Pickhardt; Nicholas Petrick
Journal:  Acad Radiol       Date:  2010-06-12       Impact factor: 3.173

9.  CT colonography: effect of computer-aided detection of colonic polyps as a second and concurrent reader for general radiologists with moderate experience in CT colonography.

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

10.  Does a computer-aided detection algorithm in a second read paradigm enhance the performance of experienced computed tomography colonography readers in a population of increased risk?

Authors:  Ayso H de Vries; Sebastiaan Jensch; Marjolein H Liedenbaum; Jasper Florie; Chung Y Nio; Roel Truyen; Shandra Bipat; Evelien Dekker; Paul Fockens; Lubbertus C Baak; Jaap Stoker
Journal:  Eur Radiol       Date:  2008-11-04       Impact factor: 5.315

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