Adult; Aged; Aged, 80 and over; Artificial Intelligence; Colonic Polyps/radiography; Colonography, Computed Tomographic/methods; Female; Humans; Male; Middle Aged; Observer Variation; Professional Competence; Radiographic Image Interpretation, Computer-Assisted/methods; Reproducibility of Results; Sensitivity and Specificity; Task Performance and Analysis; Time Factors; User-Computer Interface
Abstract :
[en] PURPOSE: To prospectively evaluate the learning curves and reading times of inexperienced readers who used the virtual dissection reading method for retrospective computed tomographic (CT) colonography data sets, with and without concurrent computer-aided detection (CAD). MATERIALS AND METHODS: An Institutional Review Board approved this study; informed consent was waived. Four radiologists without experience in CT colonography evaluated 100 optical colonoscopy-proved data sets of 100 patients (49 men, 51 women; mean age, 59 years +/- 13 [standard deviation]; range, 21-85 years) by using the virtual dissection reading method. Two readers used concurrent CAD. Data sets were read during five consecutive 1-day sessions (20 data sets per session). Polyp detection and false-positive rates, receiver operating characteristics (ROCs), and reading times were calculated for individual, CAD group, and non-CAD group readings. Diagnostic values were compared by calculating the 95% confidence intervals (CIs) around the relative risk. Areas under ROC curves (AUCs) (Hanley and McNeil for paired analysis and z statistics for unpaired analysis) and reading times (Wilcoxon signed rank test) were compared across the sessions, within each session and for the whole study. RESULTS: The range of detection rates was 79 of 111 (.71 [95% CI: .61, .79]) to 91 of 111 (.82 [95% CI: .73, .88]). The range of false-positive rates was 17 of 111 (.15 [95% CI: .09, .23]) to 22 of 111 (.20 [95% CI: .12, .28]). All readers' AUCs rose from session 1 to session 4; this rise was significant (P < .05) for the non-CAD group. Only during session 1 was the CAD group AUC (.83) higher than the non-CAD group AUC (.54) (P < .05). Comparison of CAD and non-CAD reading times showed no significant difference for the whole study or during each session (P > .05). CONCLUSION: The virtual dissection reading technique allows short learning curves, which may be improved by the concurrent use of CAD, without significant effect on average reading time.
Disciplines :
Radiology, nuclear medicine & imaging
Author, co-author :
Hock, Danielle
Ouhadi, Roxanne
Materne, Roland
Aouchria, Amir-Samy
Mancini, Isabelle
Broussaud, Thomas
Magotteaux, Paul
NCHIMI LONGANG, Alain ; Centre Hospitalier Universitaire de Liège - CHU > Radiodiagnostic
Language :
English
Title :
Virtual dissection CT colonography: evaluation of learning curves and reading times with and without computer-aided detection.
Publication date :
2008
Journal title :
Radiology
ISSN :
0033-8419
eISSN :
1527-1315
Publisher :
Radiological Society of North America, United States - Pennsylvania
Johnson CD, Dachman AH. CT colonography: the next colon screening examination? Radiology 2000;216(2):331-341.
Pickhardt PJ, Choi JR, Hwang I, et al. Computed tomographic virtual colonoscopy to screen for colorectal neoplasia in asymptomatic adults. N Engl J Med 2003;349(23):2191-2200.
Nicholson FB, Barro JL, Bartram CI, et al. The role of CT colonography in colorectal cancer screening. Am J Gastroenterol 2005;100(10):2315-2323.
Taylor SA, Laghi A, Lefere P, Halligan S, Stoker J. European Society of Gastrointestinal and Abdominal Radiology (ESGAR): consensus statement on CT colonography. Eur Radiol 2007;17(2):575-579.
Halligan S, Altman DG, Taylor SA, et al. CT colonography in the detection of colorectal polyps and cancer: systematic review, meta-analysis, and proposed minimum data set for study level reporting. Radiology 2005;237(3):893-904.
Pescatore P, Glucker T, Delarive J, et al. Diagnostic accuracy and interobserver agreement of CT colonography (virtual colonoscopy). Gut 2000;47(1):126-130.
McFarland EG, Brink JA, Pilgram TK, et al. Spiral CT colonography: reader agreement and diagnostic performance with two- and three-dimensional image-display techniques. Radiology 2001;218(2):375-383.
Mulhall BP, Veerappan GR, Jackson JL. Meta-analysis: computed tomographic colonography. Ann Intern Med 2005;142(8):635-650.
Neri E, Vannozzi F, Vagli P, Bardine A, Bartolozzi C. Time efficiency of CT colonography: 2D vs 3D visualization. Comput Med Imaging Graph 2006;30(3):175-180.
Macari M, Milano A, Lavelle M, Berman P, Megibow AJ. Comparison of time-efficient CT colonography with two- and three-dimensional colonic evaluation for detecting colorectal polyps. AJR Am J Roentgenol 2000;174(6):1543-1549.
Jensch S, van Gelder RE, Florie J, et al. Performance of radiographers in the evaluation of CT colonographic images. AJR Am J Roentgenol 2007;188(3):W249-W255.
Gluecker T, Meuwly JY, Pescatore P, et al. Effect of investigator experience in CT colonography. Eur Radiol 2002;12(6):1405-1409.
Halligan S, Altman DG, Mallett S, et al. Computed tomographic colonography: assessment of radiologist performance with and without computer-aided detection. Gastroenterology 2006;131(6):1690-1699.
Taylor SA, Halligan S, Burling D, et al. CT colonography: effect of experience and training on reader performance. Eur Radiol 2004;14(6):1025-1033.
Vos FM, van Gelder RE, Serlie IW, et al. Three-dimensional display modes for CT colonography: conventional 3D virtual colonoscopy versus unfolded cube projection. Radiology 2003;228(3):878-885.
Rottgen R, Fischbach F, Plotkin M, et al. CT colonography using different reconstruction modi. Clin Imaging 2005;29(3):195-199.
Silva AC, Wellnitz CV, Hara AK. Three-dimensional virtual dissection at CT colonography: unraveling the colon to search for lesions. RadioGraphics 2006;26(6):1669-1686.
Johnson KT, Johnson CD, Fletcher JG, MacCarty RL, Summers RL. CT colonography using 360-degree virtual dissection: a feasibility study. AJR Am J Roentgenol 2006;186(1):90-95.
Summers RM, Jerebko AK, Franaszek M, Malley JD, Johnson CD. Colonic polyps: complementary role of computer-aided detection in CT colonography. Radiology 2002;225(2):391-399.
Mani A, Napel S, Paik DS, et al. Computed tomography colonography: feasibility of computer-aided polyp detection in a "first reader" paradigm. J Comput Assist Tomogr 2004;28(3):318-326.
Taylor SA, Halligan S, Slater A, et al. Polyp detection with CT colonography: primary 3D endoluminal analysis versus primary 2D transverse analysis with computer-assisted reader software. Radiology 2006;239(3):759-767.
Shi R, Schraedley-Desmond P, Napel S, et al. CT colonography: influence of 3D viewing and polyp candidate features on interpretation with computer-aided detection. Radiology 2006;239(3):768-776.
Hanley JA, McNeil BJ. A method of comparing the areas under receiver operating characteristic curves derived from the same cases. Radiology 1983;148(3):839-843.
Fidler JL, Fletcher JG, Johnson CD, et al. Understanding interpretive errors in radiologists learning computed tomography colonography. Acad Radiol 2004;11(7):750-756.
Chan HP, Doi K, Vyborny CJ, et al. Improvement in radiologists' detection of clustered microcalcifications on mammograms: the potential of computer-aided diagnosis. Invest Radiol 1990;25(10):1102-1110.
Chan HP, Sahiner B, Helvie MA, et al. Improvement of radiologists' characterization of mammographic masses by using computer-aided diagnosis: an ROC study. Radiology 1999;212(3):817-827.
Ko JP, Betke M. Chest CT: automated nodule detection and assessment of change over time - preliminary experience. Radiology 2001;218(1):267-273.
Huo Z, Giger ML, Vyborny CJ, Metz CE. Breast cancer: effectiveness of computer-aided diagnosis observer study with independent database of mammograms. Radiology 2002;224(2):560-568.
Awai K, Murao K, Ozawa A, et al. Pulmonary nodules at chest CT: effect of computer-aided diagnosis on radiologists' detection performance. Radiology 2004;230(2):347-352.
Summers RM, Beaulieu CF, Pusanik LM, et al. Automated polyp detector for CT colonography: feasibility study. Radiology 2000;216(1):284-290.
Ling SH, Summers RM, Loew MH, McCollough CH, Johnson CD. Computer-aided detection of polyps in a colon phantom: effect of scan orientation, polyp size, collimation, and dose. J Comput Assist Tomogr 2002;26(6):1013-1018.
Bogoni L, Cathier P, Dundar M, et al. Computer-aided detection (CAD) for CT colonography: a tool to address a growing need. Br J Radiol 2005;78(spec issue 1):S57-S62.
Hoppe H, Quattropani C, Spreng A, Mattich J, Netzer P, Dinkel HP. Virtual colon dissection with CT colonography compared with axial interpretation and conventional colonoscopy: preliminary results. AJR Am J Roentgenol 2004;182(5):1151-1158.
Carrascosa P, Capuñay C, López EM, Ulla M, Castiglioni R, Carrascosa J. Multidetector CT colonoscopy: evaluation of the perspective-filet view virtual colon dissection technique for the detection of elevated lesions. Abdom Imaging 2007;32(5):582-588.
Kim SH, Lee JM, Eun HW, et al. Two- versus three-dimensional colon evaluation with recently developed virtual dissection software for CT colonography. Radiology 2007;244(3):852-864.
European Society of Gastrointestinal and Abdominal Radiology CT Colonography Group Investigators. Effect of directed training on reader performance for CT colonography: multicenter study. Radiology 2007;242(1):152-161.
Burling D, Halligan S, Altman DG, et al. CT colonography interpretation times: effect of reader experience, fatigue, and scan findings in a multi-centre setting. Eur Radiol 2006;16(8):1745-1749.
This website uses cookies to improve user experience. Read more
Save & Close
Accept all
Decline all
Show detailsHide details
Cookie declaration
About cookies
Strictly necessary
Performance
Strictly necessary cookies allow core website functionality such as user login and account management. The website cannot be used properly without strictly necessary cookies.
This cookie is used by Cookie-Script.com service to remember visitor cookie consent preferences. It is necessary for Cookie-Script.com cookie banner to work properly.
Performance cookies are used to see how visitors use the website, eg. analytics cookies. Those cookies cannot be used to directly identify a certain visitor.
Used to store the attribution information, the referrer initially used to visit the website
Cookies are small text files that are placed on your computer by websites that you visit. Websites use cookies to help users navigate efficiently and perform certain functions. Cookies that are required for the website to operate properly are allowed to be set without your permission. All other cookies need to be approved before they can be set in the browser.
You can change your consent to cookie usage at any time on our Privacy Policy page.