Artificial intelligence as diagnostic aiding tool in cases of Prostate Imaging Reporting and Data System category 3: the results of retrospective multi-center cohort study.

PURPOSE: To study the effect of artificial intelligence (AI) on the diagnostic performance of radiologists in interpreting prostate mpMRI images of the PI-RADS 3 category. METHODS: In this multicenter study, 16 radiologists were invited to interpret prostate mpMRI cases with and without AI. The study included a total of 87 cases initially diagnosed as PI-RADS 3 by radiologists without AI, with 28 cases being clinically significant cancers (csPCa) and 59 cases being non-csPCa. The study compared the diagnostic efficacy between readings without and with AI, the reading time, and confidence levels. RESULTS: AI changed the diagnosis in 65 out of 87 cases. Among the 59 non-csPCa cases, 41 were correctly downgraded to PI-RADS 1-2, and 9 were incorrectly upgraded to PI-RADS 4-5. For the 28 csPCa cases, 20 were correctly upgraded to PI-RADS 4-5, and 5 were incorrectly downgraded to PI-RADS 1-2. Radiologists assisted by AI achieved higher diagnostic specificity and accuracy than those without AI [0.695 vs 0.000 and 0.736 vs 0.322, both P < 0.001]. Sensitivity with AI was not significantly different from that without AI [0.821 vs 1.000, P = 1.000]. AI reduced reading time significantly compared to without AI (mean: 351 seconds, P < 0.001). The diagnostic confidence score with AI was significantly higher than that without AI (Cohen Kappa: -0.016). CONCLUSION: With the help of AI, there was an improvement in the diagnostic accuracy of PI-RADS category 3 cases by radiologists. There is also an increase in diagnostic efficiency and diagnostic confidence.

A multicenter study of artificial intelligence-aided software for detecting visible clinically significant prostate cancer on mpMRI.

BACKGROUND: AI-based software may improve the performance of radiologists when detecting clinically significant prostate cancer (csPCa). This study aims to compare the performance of radiologists in detecting MRI-visible csPCa on MRI with and without AI-based software. MATERIALS AND METHODS: In total, 480 multiparametric MRI (mpMRI) images were retrospectively collected from eleven different MR devices, with 349 csPCa lesions in 180 (37.5%) cases. The csPCa areas were annotated based on pathology. Sixteen radiologists from four hospitals participated in reading. Each radiologist was randomly assigned to 30 cases and diagnosed twice. Half cases were interpreted without AI, and the other half were interpreted with AI. After four weeks, the cases were read again in switched mode. The mean diagnostic performance was compared using sensitivity and specificity on lesion level and patient level. The median reading time and diagnostic confidence were assessed. RESULTS: On lesion level, AI-aided improved the sensitivity from 40.1% to 59.0% (18.9% increased; 95% confidence interval (CI) [11.5, 26.1]; p < .001). On patient level, AI-aided improved the specificity from 57.7 to 71.7% (14.0% increase, 95% CI [6.4, 21.4]; p < .001) while preserving the sensitivity (88.3% vs. 93.9%, p = 0.06). AI-aided reduced the median reading time of one case by 56.3% from 423 to 185 s (238-s decrease, 95% CI [219, 260]; p < .001), and the median diagnostic confidence score was increased by 10.3% from 3.9 to 4.3 (0.4-score increase, 95% CI [0.3, 0.5]; p < .001). CONCLUSIONS: AI software improves the performance of radiologists by reducing false positive detection of prostate cancer patients and also improving reading times and diagnostic confidence. CLINICAL RELEVANCE STATEMENT: This study involves the process of data collection, randomization and crossover reading procedure.

Associations between carotid atherosclerotic plaque characteristics determined by magnetic resonance imaging and improvement of cognition in patients undergoing carotid endarterectomy.

Background: To determine the predictive value of carotid plaque characteristics for the improvement of cognition in patients with moderate-to-severe carotid stenosis after carotid endarterectomy (CEA), using vessel wall magnetic resonance imaging (MRI). Methods: This was a prospective cohort study. Patients with unilateral, moderate-to-severe carotid stenosis referred to the Peking University Third Hospital for CEA were prospectively recruited and underwent carotid vessel wall MRI within 1 week before CEA. We performed Montreal Cognitive Assessment (MoCA) within 1 week before and 3-4 days after CEA. The morphological and compositional characteristics of carotid plaques on MRI were evaluated. Improvement of cognition was defined as >10% increase of the total MoCA score after CEA compared with baseline. Carotid plaque characteristics were compared between patients with and without cognitive improvement. Results: In total, 105 patients (91 males; mean age, 65.5±8.4 years) were included. The volume {48.0 [interquartile range (IQR), 21.0 to 91.6] vs. 16.3 (IQR, 8.1 to 53.1) mm3; P=0.005} and cumulative slice [4.0 (IQR, 3.0 to 7.0) vs. 3.0 (IQR, 2.0 to 5.0); P=0.019] of carotid calcification, and maximum percentage of calcification area [13.1% (IQR, 6.0% to 19.8%) vs. 6.2% (IQR, 3.7% to 10.8%); P=0.004] were significantly smaller in participants with cognitive improvement compared to those without. Univariate logistic regression analysis showed that volume [odds ratio (OR) =0.994; 95% confidence interval (CI): 0.989 to 1.000; P=0.043] and cumulative slice (OR =0.823; 95% CI: 0.698 to 0.970; P=0.020) of carotid calcification, and maximum percentage of calcification area (OR =0.949; 95% CI: 0.909 to 0.991; P=0.018) were significantly correlated with cognitive improvement. After adjusting for confounding factors, these associations remained statistically or marginally significant (volume: OR =0.994; 95% CI: 0.988 to 1.000; P=0.057; maximum percentage of calcification area: OR =0.937; 95% CI: 0.890 to 0.987; P=0.014; and cumulative slice: OR =0.791; 95% CI: 0.646 to 0.967; P=0.022). No significant associations were found between other plaque characteristics and cognitive improvement (all P>0.05). Conclusions: More than half of the participants with unilateral, moderate-to-severe carotid atherosclerotic stenosis had cognitive improvement. The size of calcification might be an effective indicator of cognitive improvement after CEA.