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Purpose: PI-RADS 4 lesions are considered to have a "high" likelihood of clinically-significant prostate cancer (csPCa). However, patients undergoing targeted biopsy have a range of histologic findings. Understanding discordant cases is critical to improve diagnostic accuracy and inform subsequent management. We studied early findings from implementation of a multidisciplinary Quality Improvement (QI) protocol for reconciling discordance and evaluate the potential heterogeneity of PI-RADS 4., Methods: Patients with mpMRI PI-RADS 4 lesions undergoing fusion-targeted biopsy from January 2017 to May 2021 were retrospectively reviewed. The discordant targeted biopsy pathology (benign/GG1) was evaluated utilizing a QI protocol and all lesions were subcategorized based on ADC values. Positive Predictive Value (PPV) for PI-RADS 4 lesions overall and the Cancer Detection Rate (CDR) for subcategorized lesions were calculated., Results: 248 patients with 286 lesions were reviewed. Prior to re-review, PI-RADS 4 PPV for ≥ GG1 and ≥ GG2 lesions were 0.55 and 0.34, increasing to 0.67 and 0.43 following reconciliation. Lesion subcategorization based on ADC value as higher suspicion (4+) and lower suspicion (4-) resulted in 158 and 117 lesions, with reverse-fusion analysis revealing that 61% and 17% of lesions contained csPCa, respectively. Subgroup analysis among PI-RADS 4+ lesions led to an increase in the CDR to 75% and 61% for ≥ GG1 and ≥ GG2., Conclusion: Use of multidisciplinary QI protocol to review discordance cases of PI-RADS 4 improves diagnostic accuracy and guides subsequent management. Our findings highlight the known heterogeneity of this category with reference to csPCa CDR, suggesting the potential value of PI-RADS 4 subcategorization., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. In the interest of full disclosure, Dr. Gupta does disclose that he is a consultant to Philips., (Copyright © 2023 Elsevier B.V. All rights reserved.)

Purpose: Manual measurement of body composition on computed tomography (CT) is time-consuming, limiting its clinical use. We validate a software program, Automatic Body composition Analyzer using Computed tomography image Segmentation (ABACS), for the automated measurement of body composition by comparing its performance to manual segmentation in a cohort of patients with bladder cancer., Method: We performed a retrospective analysis of 285 patients treated for bladder cancer at the Duke University Health System from 1996 to 2017. Abdominal CT images were manually segmented at L3 using Slice-O-Matic. Automated segmentation was performed with ABACS on the same L3-level images. Measures of interest were skeletal muscle (SM) area, subcutaneous adipose tissue (SAT) area, and visceral adipose tissue (VAT) area. SM index, SAT index, and VAT index were calculated by dividing component areas by patient height 2 (m 2 ). Patients were dichotomized as sarcopenic, having excessive subcutaneous fat, or having excessive visceral fat using published cut-off values. Agreement between manual and automated segmentation was assessed using the Pearson product-moment correlation coefficient (PPMCC), the interclass correlation coefficient (ICC3), and the kappa statistic (κ)., Results: There was strong agreement between manual and automatic segmentation, with PPMCCs > 0.90 and ICC3s > 0.90 for SM, SAT, and VAT areas. Categorization of patients as sarcopenic (κ = 0.73), having excessive subcutaneous fat (κ = 0.88), or having excessive visceral fat (κ = 0.90) displayed high agreement between methods., Conclusions: Automated segmentation of body composition measures on CT using ABACS performs similarly to manual analysis and may expedite data collection in body composition research., (Copyright © 2022 Elsevier B.V. All rights reserved.)