CT colonography: Preliminary assessment of a double-read paradigm that uses computer-aided detection as the first reader

Purpose: To compare diagnostic performance and time efficiency of double-reading first-reader computer-aided detection (CAD) (DR FR CAD) followed by radiologist interpretation with that of an unassisted read using segmentally un-blinded colonoscopy as reference standard. Materials and Methods: The local ethical committee approved this study. Written consent to use examinations was obtained from patients. Three experienced radiologists searched for polyps 6 mm or larger in 155 computed tomographic (CT) colonographic studies (57 containing 10 masses and 79 polyps >= 6 mm). Reading was randomized to either unassisted read or DR FR CAD. Data sets were reread 6 weeks later by using the opposite paradigm. DR FR CAD consists of evaluation of CAD prompts, followed by fast two-dimensional review for mass detection. CAD sensitivity was calculated. Readers' diagnoses and reviewing times with and without CAD were compared by using McNemar and Student t tests, respectively. Association between missed polyps and lesion characteristics was explored with multiple regression analysis. Results: With mean rate of 19 (standard deviation, 14; median, 15; range, 4-127) false-positive results per patient, CAD sensitivity was 90% for lesions 6 mm or larger. Readers' sensitivity and specificity for lesions 6 mm or larger were 74% (95% confidence interval [CI]: 65%, 84%) and 93% (95% CI: 89%, 97%), respectively, for the unassisted read and 77% (95% CI: 67%, 85%) and 90% (95% CI: 85%, 95%), respectively, for DR FR CAD (P = .343 and P = .189, respectively). Overall unassisted and DR FR CAD reviewing times were similar (243 vs 239 seconds; P = .623); DR FR CAD was faster when the number of CAD marks per patient was 20 or fewer (187 vs 220 seconds, P < .01). Odds ratio of missing a polyp with CAD decreased as polyp size increased (0.6) and for polyps visible on both prone and supine scans (0.12); it increased for flat lesions (9.1). Conclusion: DR FR CAD paradigm had similar performance compared with unassisted interpretation but better time efficiency when 20 or fewer CAD prompts per patient were generated. (C) RSNA, 201

Optimization of Computer Aided Detection systems: an evolutionary approach

Computer Aided Diagnosis (CAD) systems are designed to aid the radiologist in interpreting medical images. They are usually based on lesion detection and segmentation algorithms whose performance depends on a large number of parameters. While time consuming and sub-optimal, manual adjustment is still widely used to adjust parameter values. Genetic or evolutionary algorithms (GA) are effective optimization methods that mimic biological evolution. Genetic algorithms have been shown to efficiently manage complex search spaces, and can be applied to all kinds of objective functions, including discontinuous, nondifferentiable, or highly nonlinear ones. In this study, we have adopted an evolutionary approach to the problem of parameter optimization. We show that the genetic algorithm is able to effectively converge to a better solution than manual optimization on a case study for digital breast tomosynthesis CAD. Parameter optimization was framed as a constrained optimization problem, where the function to be maximized was defined as weighted sum of sensitivity, false positive rate and segmentation accuracy. A modified Dice coefficient was defined to assess the segmentation quality of individual lesions. Finally, all viable solutions evaluated by the GA were studied by means of exploratory data analysis techniques, such as association rules, to gain useful insight on the strength of the influence of each parameter on overall algorithm performance. We showed that this combination was able to identify multiple ranges of viable solutions with good segmentation accuracy