Literature DB >> 25384290

CT colonography with computer-aided detection: recognizing the causes of false-positive reader results.

Igor Trilisky1, Kristen Wroblewski, Michael W Vannier, John M Horne, Abraham H Dachman.   

Abstract

Computed tomography (CT) colonography is a screening modality used to detect colonic polyps before they progress to colorectal cancer. Computer-aided detection (CAD) is designed to decrease errors of detection by finding and displaying polyp candidates for evaluation by the reader. CT colonography CAD false-positive results are common and have numerous causes. The relative frequency of CAD false-positive results and their effect on reader performance on the basis of a 19-reader, 100-case trial shows that the vast majority of CAD false-positive results were dismissed by readers. Many CAD false-positive results are easily disregarded, including those that result from coarse mucosa, reconstruction, peristalsis, motion, streak artifacts, diverticulum, rectal tubes, and lipomas. CAD false-positive results caused by haustral folds, extracolonic candidates, diminutive lesions (<6 mm), anal papillae, internal hemorrhoids, varices, extrinsic compression, and flexural pseudotumors are almost always recognized and disregarded. The ileocecal valve and tagged stool are common sources of CAD false-positive results associated with reader false-positive results. Nondismissable CAD soft-tissue polyp candidates larger than 6 mm are another common cause of reader false-positive results that may lead to further evaluation with follow-up CT colonography or optical colonoscopy. Strategies for correctly evaluating CAD polyp candidates are important to avoid pitfalls from common sources of CAD false-positive results. ©RSNA, 2014.

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Year:  2014        PMID: 25384290      PMCID: PMC4386870          DOI: 10.1148/rg.347130053

Source DB:  PubMed          Journal:  Radiographics        ISSN: 0271-5333            Impact factor:   5.333


  59 in total

1.  Small and diminutive polyps detected at screening CT colonography: a decision analysis for referral to colonoscopy.

Authors:  Perry J Pickhardt; Cesare Hassan; Andrea Laghi; Angelo Zullo; David H Kim; Franco Iafrate; Sergio Morini
Journal:  AJR Am J Roentgenol       Date:  2008-01       Impact factor: 3.959

Review 2.  CT colonography: visualization methods, interpretation, and pitfalls.

Authors:  Abraham H Dachman; Philippe Lefere; Stefaan Gryspeerdt; Martina Morin
Journal:  Radiol Clin North Am       Date:  2007-03       Impact factor: 2.303

3.  Computed tomographic virtual colonoscopy computer-aided polyp detection in a screening population.

Authors:  Ronald M Summers; Jianhua Yao; Perry J Pickhardt; Marek Franaszek; Ingmar Bitter; Daniel Brickman; Vamsi Krishna; J Richard Choi
Journal:  Gastroenterology       Date:  2005-12       Impact factor: 22.682

4.  Electronic cleansing for CT colonography: does it help CAD software performance in a high-risk population for colorectal cancer?

Authors:  Jae Yeon Wi; Se Hyung Kim; Jae Young Lee; Sang Gyun Kim; Joon Koo Han; Byung Ihn Choi
Journal:  Eur Radiol       Date:  2010-03-23       Impact factor: 5.315

5.  Effect of computer-aided detection for CT colonography in a multireader, multicase trial.

Authors:  Abraham H Dachman; Nancy A Obuchowski; Jeffrey W Hoffmeister; J Louis Hinshaw; Michael I Frew; Thomas C Winter; Robert L Van Uitert; Senthil Periaswamy; Ronald M Summers; Bruce J Hillman
Journal:  Radiology       Date:  2010-07-27       Impact factor: 11.105

6.  Computed tomographic colonography (virtual colonoscopy): a multicenter comparison with standard colonoscopy for detection of colorectal neoplasia.

Authors:  Peter B Cotton; Valerie L Durkalski; Benoit C Pineau; Yuko Y Palesch; Patrick D Mauldin; Brenda Hoffman; David J Vining; William C Small; John Affronti; Douglas Rex; Kenyon K Kopecky; Susan Ackerman; J Steven Burdick; Cecelia Brewington; Mary A Turner; Alvin Zfass; Andrew R Wright; Revathy B Iyer; Patrick Lynch; Michael V Sivak; Harold Butler
Journal:  JAMA       Date:  2004-04-14       Impact factor: 56.272

7.  Efficacy of computer-aided detection as a second reader for 6-9-mm lesions at CT colonography: multicenter prospective trial.

Authors:  Daniele Regge; Patrizia Della Monica; Giovanni Galatola; Cristiana Laudi; Antonella Zambon; Loredana Correale; Roberto Asnaghi; Brunella Barbaro; Claudia Borghi; Delia Campanella; Maria Carla Cassinis; Riccardo Ferrari; Andrea Ferraris; Cesare Hassan; Rita Golfieri; Franco Iafrate; Gabriella Iussich; Andrea Laghi; Roberto Massara; Emanuele Neri; Lapo Sali; Silvia Venturini; Giovanni Gandini
Journal:  Radiology       Date:  2012-11-14       Impact factor: 11.105

8.  Estimation of impact of American College of Radiology recommendations on CT colonography reporting for resection of high-risk adenoma findings.

Authors:  Douglas K Rex; Andrew J Overhiser; Shawn C Chen; Oscar W Cummings; Thomas M Ulbright
Journal:  Am J Gastroenterol       Date:  2009-01       Impact factor: 10.864

9.  Colonoscopic miss rates determined by direct comparison of colonoscopy with colon resection specimens.

Authors:  Georges Postic; David Lewin; Charles Bickerstaff; Michael B Wallace
Journal:  Am J Gastroenterol       Date:  2002-12       Impact factor: 10.864

10.  Morphology, anatomic distribution and cancer potential of colonic polyps.

Authors:  H Shinya; W I Wolff
Journal:  Ann Surg       Date:  1979-12       Impact factor: 12.969

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  3 in total

1.  Dual-energy CT characteristics of colon and rectal cancer allows differentiation from stool by dual-source CT.

Authors:  İlknur Özdeniz; İlkay S İdilman; Seyfettin Köklü; Erhan Hamaloğlu; Mustafa Özmen; Deniz Akata; Muşturay Karçaaltıncaba
Journal:  Diagn Interv Radiol       Date:  2017 Jul-Aug       Impact factor: 2.630

2.  CT colonography has low sensitivity but high specificity in the detection of internal hemorrhoids.

Authors:  Lukas Lambert; Jiri Jahoda; Gabriela Grusova; Pavel Hrabak; Ales Novotny; Andrea Burgetova
Journal:  Diagn Interv Radiol       Date:  2020-03       Impact factor: 2.630

Review 3.  Challenges and opportunities for artificial intelligence in oncological imaging.

Authors:  H M C Cheung; D Rubin
Journal:  Clin Radiol       Date:  2021-04-24       Impact factor: 3.389

  3 in total

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