Computer-aided detection (CAD) for CT colonography: a tool to address a growing need

Bogoni, Luca; Cathier, Pascal; Dündar, Murat; Jerebko, Anna K.; Lakare, Sarang; Liang, Jianming; Periaswamy, Senthil; Baker, Mark E.; Macari, Michael

doi:10.1259/bjr/25777270

Cited by 68 publications

(46 citation statements)

References 19 publications

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“…There is no single standard metric for comparing results in clinical CAD trials, which is why it is important to present the results by using several different metrics. There are results of remarkably few similar studies about the use of CAD with which to compare our results, partly because most clinical studies about CAD included much smaller cohorts of patients and readers and methods that focused on a specifi c question, such as CAD reading mode, CAD for training ( 12,32 ), or use in different or special viewing methods (3)(4)(5)(6)(7)(8)(9)(10)(12)(13)(14)(15)(16)(17)(18)(19)(20)(21)(22)33 ). The largest prior multireader, multicase reader CAD trial in a screening cohort, that of Petrick et al ( 10 ), was conducted by using a subset of 60 patients in which four readers used CAD in a second-reader mode and analyzed only neoplastic polyps (not all polyps * Small polyps are larger than 6 mm but smaller than 10 mm.…”

Section: Gastrointestinal Imaging: Computer-aided Detection For Ct Comentioning

confidence: 99%

Effect of Computer-aided Detection for CT Colonography in a Multireader, Multicase Trial

Dachman¹,

Obuchowski²,

Hoffmeister³

et al. 2010

Radiology

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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 fi ndings positive for polyps, 74 polyps of 6 mm or larger were observed in 65 colonic segments; in 48 patients with fi ndings 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 signifi cantly 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 specifi city of readers by 0.025 ( P = .05). Conclusion:Use of CAD resulted in a signifi cant 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 specifi city by a small amount.q RSNA, 2010 Supplemental material: http://radiology.rsna.org/lookup /suppl

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Section: Gastrointestinal Imaging: Computer-aided Detection For Ct Comentioning

confidence: 99%

Effect of Computer-aided Detection for CT Colonography in a Multireader, Multicase Trial

Dachman¹,

Obuchowski²,

Hoffmeister³

et al. 2010

Radiology

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“…It is necessary and urgent to extract describable features from ovarian PAT images for cancer diagnosis with computer aided detection (CAD) systems, which have been demonstrated a positive result on early cancer detection [4,5]. Unfortunately, literature is scarce referring to utilization of CAD in ovarian cancer detection.…”

Section: Introductionmentioning

confidence: 99%

Tuning to optimize SVM approach for assisting ovarian cancer diagnosis with photoacoustic imaging

Wang

Lei

et al. 2015

BME

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Abstract. Support vector machine (SVM) is one of the most effective classification methods for cancer detection. The efficiency and quality of a SVM classifier depends strongly on several important features and a set of proper parameters. Here, a series of classification analyses, with one set of photoacoustic data from ovarian tissues ex vivo and a widely used breast cancer dataset-the Wisconsin Diagnostic Breast Cancer (WDBC), revealed the different accuracy of a SVM classification in terms of the number of features used and the parameters selected. A pattern recognition system is proposed by means of SVM-Recursive Feature Elimination (RFE) with the Radial Basis Function (RBF) kernel. To improve the effectiveness and robustness of the system, an optimized tuning ensemble algorithm called as SVM-RFE(C) with correlation filter was implemented to quantify feature and parameter information based on cross validation. The proposed algorithm is first demonstrated outperforming SVM-RFE on WDBC. Then the best accuracy of 94.643% and sensitivity of 94.595% were achieved when using SVM-RFE(C) to test 57 new PAT data from 19 patients. The experiment results show that the classifier constructed with SVM-RFE(C) algorithm is able to learn additional information from new data and has significant potential in ovarian cancer diagnosis.

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“…Most reader errors are avoidable and result from errors of detection (12)(13)(14). Computer-aided detection (CAD) is designed to decrease errors of detection by finding and displaying possible polyps to be evaluated by the reader (15)(16)(17)(18).…”

Section: Frequency Of Cad False-positive Resultsmentioning

confidence: 99%

CT Colonography with Computer-aided Detection: Recognizing the Causes of False-Positive Reader Results

et al. 2014

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1Computed 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 falsepositive 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.

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Computer-aided detection (CAD) for CT colonography: a tool to address a growing need

Cited by 68 publications

References 19 publications

Effect of Computer-aided Detection for CT Colonography in a Multireader, Multicase Trial

Effect of Computer-aided Detection for CT Colonography in a Multireader, Multicase Trial

Tuning to optimize SVM approach for assisting ovarian cancer diagnosis with photoacoustic imaging

CT Colonography with Computer-aided Detection: Recognizing the Causes of False-Positive Reader Results

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