Chest radiograph interpretation, assisted by a deep learning–based automatic detection algorithm, can reduce the number of overlooked lung cancers without increasing the frequency of chest CT follo... Background It is uncertain whether a deep learning–based automatic detection algorithm (DLAD) for identifying malignant nodules on chest radiographs will help diagnose lung cancers. Purpose To eval...

To assess the effect of computer-aided detection (CAD) of brain metastasis (BM) on radiologists’ diagnostic performance in interpreting three-dimensional brain magnetic resonance (MR) imaging using follow-up imaging and consensus as the reference standard. The institutional review board approved this retrospective study. The study cohort consisted of 110 consecutive patients with BM and 30 patients without BM. The training data set included MR images of 80 patients with 450 BM nodules. The test set included MR images of 30 patients with 134 BM nodules and 30 patients without BM. We developed a CAD system for BM detection using template-matching and K-means clustering algorithms for candidate detection and an artificial neural network for false-positive reduction. Four reviewers (two neuroradiologists and two radiology residents) interpreted the test set images before and after the use of CAD in a sequential manner. The sensitivity, false positive (FP) per case, and reading time were analyzed. A jackknife free-response receiver operating characteristic (JAFROC) method was used to determine the improvement in the diagnostic accuracy. The sensitivity of CAD was 87.3% with an FP per case of 302.4. CAD significantly improved the diagnostic performance of the four reviewers with a figure-of-merit (FOM) of 0.874 (without CAD) vs. 0.898 (with CAD) according to JAFROC analysis (p < 0.01). Statistically significant improvement was noted only for less-experienced reviewers (FOM without vs. with CAD, 0.834 vs. 0.877, p < 0.01). The additional time required to review the CAD results was approximately 72 sec (40% of the total review time). CAD as a second reader helps radiologists improve their diagnostic performance in the detection of BM on MR imaging, particularly for less-experienced reviewers.

OBJECTIVE. The objective of our study was to investigate the prevalence of imaging features on CT thought to be associated with femoroacetabular impingement (FAI) and the associations among them. MATERIALS AND METHODS. Of 430 consecutive adult patients (age range, 18–40 years) who underwent hip CT at our institution for hip symptoms or trauma surveillance, the asymptomatic hips were selected for the study, which resulted in the inclusion of 473 hip joints in the study group. Images were obtained using an MDCT scanner and were reformatted using a bone algorithm. The CT images were retrospectively evaluated by two radiologists for features indicative of FAI such as the alpha angle, femoral head-neck offset, acetabular version angle, and center-edge angle. The intraclass correlation coefficient was used to evaluate intra- and interobserver variabilities. Associations among these parameters were evaluated by calculating the odds ratio between each of the parameters separately. RESULTS. Of the 473 asymptomatic...

Kim J, et al. Korean J Radiol. 2015 May-Jun;16(3):541-549. http://dx.doi.org/10.3348/kjr.2015.16.3.541