Background Clinically significant prostate cancer (PCa) diagnosis at MRI requires accurate and efficient radiologic interpretation. Although artificial intelligence may assist in this task, lack of transparency has limited clinical translation. Purpose To develop an explainable artificial intelligence (XAI) model for clinically significant PCa diagnosis at biparametric MRI using Prostate Imaging Reporting and Data System (PI-RADS) features for classification justification. Materials and Methods This retrospective study included consecutive patients with histopathologic analysis-proven prostatic lesions who underwent biparametric MRI and biopsy between January 2012 and December 2017. After image annotation by two radiologists, a deep learning model was trained to detect the index lesion; classify PCa, clinically significant PCa (Gleason score ≥ 7), and benign lesions (eg, prostatitis); and justify classifications using PI-RADS features. Lesion- and patient-based performance were assessed using fivefold cross validation and areas under the receiver operating characteristic curve. Clinical feasibility was tested in a multireader study and by using the external PROSTATEx data set. Statistical evaluation of the multireader study included Mann-Whitney U and exact Fisher-Yates test. Results Overall, 1224 men (median age, 67 years; IQR, 62-73 years) had 3260 prostatic lesions (372 lesions with Gleason score of 6; 743 lesions with Gleason score of ≥ 7; 2145 benign lesions). XAI reliably detected clinically significant PCa in internal (area under the receiver operating characteristic curve, 0.89) and external test sets (area under the receiver operating characteristic curve, 0.87) with a sensitivity of 93% (95% CI: 87, 98) and an average of one false-positive finding per patient. Accuracy of the visual and textual explanations of XAI classifications was 80% (1080 of 1352), confirmed by experts. XAI-assisted readings improved the confidence (4.1 vs 3.4 on a five-point Likert scale; P = .007) of nonexperts in assessing PI-RADS 3 lesions, reducing reading time by 58 seconds (P = .009). Conclusion The explainable AI model reliably detected and classified clinically significant prostate cancer and improved the confidence and reading time of nonexperts while providing visual and textual explanations using well-established imaging features. © RSNA, 2023 Supplemental material is available for this article. See also the editorial by Chapiro in this issue.

Purpose: To establish magnetic resonance (MR)–based molecular imaging paradigms for the noninvasive monitoring of extracellular pH (pHe) as a functional surrogate biomarker for metabolic changes induced by locoregional therapy of liver cancer. Experimental Design: Thirty-two VX2 tumor-bearing New Zealand white rabbits underwent longitudinal imaging on clinical 3T-MRI and CT scanners before and up to 2 weeks after complete conventional transarterial chemoembolization (cTACE) using ethiodized oil (lipiodol) and doxorubicin. MR-spectroscopic imaging (MRSI) was employed for pHe mapping. Multiparametric MRI and CT were performed to quantify tumor enhancement, diffusion, and lipiodol coverage of the tumor posttherapy. In addition, incomplete cTACE with reduced chemoembolic doses was applied to mimic undertreatment and exploit pHe mapping to detect viable tumor residuals. Imaging findings were correlated with histopathologic markers indicative of metabolic state (HIF-1α, GLUT-1, and LAMP-2) and viability (proliferating cell nuclear antigen and terminal deoxynucleotidyl-transferase dUTP nick-end labeling). Results: Untreated VX2 tumors demonstrated a significantly lower pHe (6.80 ± 0.09) than liver parenchyma (7.19 ± 0.03, P < 0.001). Upregulation of HIF-1α, GLUT-1, and LAMP-2 confirmed a hyperglycolytic tumor phenotype and acidosis. A gradual tumor pHe increase toward normalization similar to parenchyma was revealed within 2 weeks after complete cTACE, which correlated with decreasing detectability of metabolic markers. In contrast, pHe mapping after incomplete cTACE indicated both acidic viable residuals and increased tumor pHe of treated regions. Multimodal imaging revealed durable tumor devascularization immediately after complete cTACE, gradually increasing necrosis, and sustained lipiodol coverage of the tumor. Conclusions: MRSI-based pHe mapping can serve as a longitudinal monitoring tool for viable tumors. As most liver tumors are hyperglycolytic creating microenvironmental acidosis, therapy-induced normalization of tumor pHe may be used as a functional biomarker for positive therapeutic outcome.

Importance: The magnetic resonance imaging (MRI) pathway for diagnosing clinically significant prostate cancer (csPCa; defined as International Society of Urological Pathology grade group ≥2) uses multiparametric MRI (mpMRI) for prostate biopsy (PB) decision-making. However, the intermediate impact on patient outcomes in men with negative MRI results avoiding PB and men with positive MRI results without PCa remains unknown.Objective: To assess the feasibility and safety of a community-based MRI diagnostic strategy in men with suspected PCa using 3-year active monitoring.Design, Setting, and Participants: This multisite, longitudinal cohort trial took place across 54 community-based urology practices and 2 radiology imaging centers at a referral academic institution in Berlin, Germany. Eligible participants aged 18 to 75 years with clinically suspected PCa were enrolled between September 2016 and December 2017 and monitored for 3 years. Final analysis was reported on December 23, 2023.Exposures: Participants underwent 3-T mpMRI. Men with findings suspected to be PCa were recommended for targeted PB (diagnostic phase). Men with negative mpMRI results or positive mpMRI results with benign findings at PB were systematically monitored for 3 years (monitoring phase). Clinical visits were recommended every 6 months.Main Outcomes and Measures: The total proportion of men avoiding PB and those with csPCa.Results: A total of 593 men (median [IQR] age, 64 [58-70] years) underwent mpMRI, with 286 (48%) having negative MRI results, 261 (44%) avoiding PB initially, and 242 (41%) avoiding PB over 3 years. csPCa was detected in 161 (27%) men after immediate PB, increasing to 172 (29%) men after 3 years. Seven men with negative MRI results were diagnosed with PCa by immediate PB (including 4 cases of csPCa), while 279 entered monitoring. Three-year monitoring was completed by 233 (84%) men, with 7 diagnoses of csPCa. Of 307 men with positive MRI results, 58 (19%) showed no PCa after immediate PB, of which 41 (71%) completed monitoring and 4 (7%) were diagnosed with csPCa.Conclusions and Relevance: In this cohort study, men with negative mpMRI results avoiding biopsy were not at elevated risk of csPCa. The study confirms the oncological safety of the prebiopsy MRI strategy of avoiding an immediate PB after negative MRI results when a programmatic safety net is in place.

Objectives: To develop and test zone-specific prostate-specific antigen density (sPSAD) combined with PI-RADS to guide prostate biopsy decision strategies (BDS). Methods: This retrospective study included consecutive patients, who underwent prostate MRI and biopsy (01/2012–10/2018). The whole gland and transition zone (TZ) were segmented at MRI using a retrained deep learning system (DLS; nnU-Net) to calculate PSAD and sPSAD, respectively. Additionally, sPSAD and PI-RADS were combined in a BDS, and diagnostic performances to detect Grade Group ≥ 2 (GG ≥ 2) prostate cancer were compared. Patient-based cancer detection using sPSAD was assessed by bootstrapping with 1000 repetitions and reported as area under the curve (AUC). Clinical utility of the BDS was tested in the hold-out test set using decision curve analysis. Statistics included nonparametric DeLong test for AUCs and Fisher-Yates test for remaining performance metrics. Results: A total of 1604 patients aged 67 (interquartile range, 61–73) with 48% GG ≥ 2 prevalence (774/1604) were evaluated. By employing DLS-based prostate and TZ volumes (DICE coefficients of 0.89 (95% confidence interval, 0.80–0.97) and 0.84 (0.70–0.99)), GG ≥ 2 detection using PSAD was inferior to sPSAD (AUC, 0.71 (0.68–0.74)/0.73 (0.70–0.76); p < 0.001). Combining PI-RADS with sPSAD, GG ≥ 2 detection specificity doubled from 18% (10–20%) to 43% (30–44%; p < 0.001) with similar sensitivity (93% (89–96%)/97% (94–99%); p = 0.052), when biopsies were taken in PI-RADS 4-5 and 3 only if sPSAD was ≥ 0.42 ng/mL/cc as compared to all PI-RADS 3-5 cases. Additionally, using the sPSAD-based BDS, false positives were reduced by 25% (123 (104–142)/165 (146–185); p < 0.001). Conclusion: Using sPSAD to guide biopsy decisions in PI-RADS 3 lesions can reduce false positives at MRI while maintaining high sensitivity for GG ≥ 2 cancers. Clinical relevance statement: Transition zone-specific prostate-specific antigen density can improve the accuracy of prostate cancer detection compared to MRI assessments alone, by lowering false-positive cases without significantly missing men with ISUP GG ≥ 2 cancers. Key Points: • Prostate biopsy decision strategies using PI-RADS at MRI are limited by a substantial proportion of false positives, not yielding grade group ≥ 2 prostate cancer. • PI-RADS combined with transition zone (TZ)-specific prostate-specific antigen density (PSAD) decreased the number of unproductive biopsies by 25% compared to PI-RADS only. • TZ-specific PSAD also improved the specificity of MRI-directed biopsies by 9% compared to the whole gland PSAD, while showing identical sensitivity.

Traditional palaeontological techniques of disease characterisation are limited to the analysis of osseous fossils, requiring several lines of evidence to support diagnoses. This study presents a novel stepwise concept for comprehensive diagnosis of pathologies in fossils by computed tomography imaging for morphological assessment combined with likelihood estimation based on systematic phylogenetic disease bracketing. This approach was applied to characterise pathologies of the left fibula and fused caudal vertebrae of the non-avian dinosaur Tyrannosaurus rex. Initial morphological assessment narrowed the differential diagnosis to neoplasia or infection. Subsequent data review from phylogenetically closely related species at the clade level revealed neoplasia rates as low as 3.1% and 1.8%, while infectious-disease rates were 32.0% and 53.9% in extant dinosaurs (birds) and non-avian reptiles, respectively. Furthermore, the survey of literature revealed that within the phylogenetic disease bracket the oldest case of bone infection (osteomyelitis) was identified in the mandible of a 275-million-year-old captorhinid eureptile Labidosaurus. These findings demonstrate low probability of a neoplastic aetiology of the examined pathologies in the Tyrannosaurus rex and in turn, suggest that they correspond to multiple foci of osteomyelitis.