Faculty Bibliography

OBJECTIVES/OBJECTIVE:Despite its high negative predictive value (NPV) for clinically significant prostate cancer (csPCa), MRI suffers from a substantial number of false positives, especially for intermediate-risk cases. In this work, we determine whether a deep learning model trained with PI-RADS-guided representation learning can disambiguate the PI-RADS 3 classification, detect csPCa from bi-parametric prostate MR images, and avoid unnecessary benign biopsies. MATERIALS AND METHODS/METHODS:This study included 28,263 MR examinations and radiology reports from 21,938 men imaged for known or suspected prostate cancer between 2015 and 2023 at our institution (21 imaging locations with 34 readers), with 6352 subsequent biopsies. We trained a deep learning model, a representation learner (RL), to learn how radiologists interpret conventionally acquired T2-weighted and diffusion-weighted MR images, using exams in which the radiologists are confident in their risk assessments (PI-RADS 1 and 2 for the absence of csPCa vs. PI-RADS 4 and 5 for the presence of csPCa, n=21,465). We then trained biopsy-decision models to detect csPCa (Gleason score ≥7) using these learned image representations, and compared them to the performance of radiologists, and of models trained on other clinical variables (age, prostate volume, PSA, and PSA density) for treatment-naïve test cohorts consisting of only PI-RADS 3 (n=253, csPCa=103) and all PI-RADS (n=531, csPCa=300) cases. RESULTS:On the 2 test cohorts (PI-RADS-3-only, all-PI-RADS), RL-based biopsy-decision models consistently yielded higher AUCs in detecting csPCa (AUC=0.73 [0.66, 0.79], 0.88 [0.85, 0.91]) compared with radiologists (equivocal, AUC=0.79 [0.75, 0.83]) and the clinical model (AUCs=0.69 [0.62, 0.75], 0.78 [0.74, 0.82]). In the PIRADS-3-only cohort, all of whom would be biopsied using our institution's standard of care, the RL decision model avoided 41% (62/150) of benign biopsies compared with the clinical model (26%, P<0.001), and improved biopsy yield by 10% compared with the PI-RADS ≥3 decision strategy (0.50 vs. 0.40). Furthermore, on the all-PI-RADS cohort, RL decision model avoided 27% of additional benign biopsies (138/231) compared to radiologists (33%, P<0.001) with comparable sensitivity (93% vs. 92%), higher NPV (0.87 vs. 0.77), and biopsy yield (0.75 vs. 0.64). The combination of clinical and RL decision models further avoided benign biopsies (46% in PI-RADS-3-only and 62% in all-PI-RADS) while improving NPV (0.82, 0.88) and biopsy yields (0.52, 0.76) across the 2 test cohorts. CONCLUSIONS:Our PI-RADS-guided deep learning RL model learns summary representations from bi-parametric prostate MR images that can provide additional information to disambiguate intermediate-risk PI-RADS 3 assessments. The resulting RL-based biopsy decision models also outperformed radiologists in avoiding benign biopsies while maintaining comparable sensitivity to csPCa for the all-PI-RADS cohort. Such AI models can easily be integrated into clinical practice to supplement radiologists' reads in general and improve biopsy yield for any equivocal decisions.

OBJECTIVE:Indeterminate lesions on prostate-specific membrane antigen (PSMA)-PET are challenging to address. We aimed to develop, implement, and evaluate a multidisciplinary consensus algorithm that integrates existing interpretation systems with multimodality imaging and clinicopathological information for interpreting indeterminate bone and lymph node lesions on PSMA-PET. MATERIALS AND METHODS/METHODS:This was a retrospective single-center study on a prospectively implemented algorithm. We included all consecutive prostate cancer patients whose PSMA-PET findings for indeterminate bone lesions or lymph nodes were discussed at a multidisciplinary tumor board (MDT) in 2024-2025. An algorithm determining the level of suspicion for metastasis was developed in a multidisciplinary fashion, incorporating lesion location, conventional imaging features, PSMA-PET characteristics, and clinicopathological information. The application of the algorithm and outcomes were documented, compared against a composite reference standard. Comparisons were made with PSMA-RADS and PROMISE V2 PSMA-expression scores. RESULTS:81 patients (median age 68, interquartile range 64-75) were included. Algorithm results were benign (48.1% [39/81]), equivocal (4.9% [4/81]), metastasis (40.7% [33/81]), and mixed (benign and metastatic lesions, 6.2% [5/81]). The algorithm was correct in 94.1% (64 of 68 patients with a sufficient reference standard). The algorithm was discordant with PSMA-RADS in 54.3% (44/81) and with PROMISE V2 PSMA-expression score in 71.6% (58/81). The frequency of equivocal lesions was lower using the algorithm (4.9% [4/81]) compared with PSMA-RADS (53.1% [43/81]) and PSMA-expression score (64.2% [52/81]). CONCLUSION/CONCLUSIONS:A multidisciplinary consensus algorithm for interpreting indeterminate bone lesions and lymph nodes on PSMA-PET was developed and implemented. Integrating clinicopathological information and multimodality imaging in an MDT setting reduced equivocal interpretations. KEY POINTS/CONCLUSIONS:Question While prostate-specific membrane antigen (PSMA)-PET has become essential in the management of prostate cancer, indeterminate bone lesions and lymph nodes remain challenging to address. Findings A multidisciplinary algorithm for interpreting indeterminate bone lesions and lymph nodes on PSMA-PET, incorporating clinicopathological information and multimodality imaging, reduced the frequency of equivocal interpretations. Clinical relevance An algorithm for interpreting indeterminate bone lesions and lymph nodes on PSMA-PET, incorporating clinicopathological information and multimodality imaging in a multidisciplinary tumor board setting, decreases the frequency of equivocal interpretations and can potentially help management decisions.

Endometriosis is a common condition primarily impacting women of childbearing age. Despite increasing awareness that endometriosis can be diagnosed non-invasively with the correct imaging techniques, there exists a significant delay in diagnosis, to the tune of 5-10 years. This gap can be narrowed by understanding that this is a disease that lends itself to pattern recognition, and learning to recognize the characteristic patterns on any imaging study will allow earlier diagnosis and prevent long-term complications that can occur with progressive, untreated endometriosis. The disease is often multifocal and thus can present with a wide array of nonspecific symptoms. When clinical findings do not suggest endometriosis, patients often undergo non-targeted imaging, such as chest, abdominal, or pelvic computed tomography (CT); Magnetic Resonance imaging (MRI) studies optimized for neurologic or musculoskeletal indications; or ultrasound (US) exams performed for palpable masses or nonspecific abdominal pain. Familiarity with endometriosis's characteristic patterns across organ systems and how it can masquerade as other diseases helps radiologists broaden their differential to include endometriosis, even on studies not originally aimed at its detection. This review article will describe those imaging findings of endometriosis affecting various organ systems that mimic other pathologies and will enable the reader to pause and question whether endometriosis should be included in the differential.

Post-radiotherapy erectile dysfunction (ED) can significantly impact the quality of life of patients with prostate cancer (PCa). Critical anatomical structures, such as the neurovascular bundle (NVB), internal pudendal arteries (IPA), penile bulb, and corporal tissues track in close proximity to the prostate, making them susceptible to radiation-related damage. This study aimed to evaluate the anatomical patterns of these structures and their relationship with the prostate, and to provide comprehensive illustrative examples on MRI. Consecutive patients with PCa who underwent MRI-linear accelerator (LINAC)-based stereotactic body radiotherapy (SBRT) in January-December 2024 were included. NVB patterns were classified into 3 categories: (1) "classical" with discrete NVB elements, (2) "adherent", dispersed and adherent to prostatic capsule, and (3) "absent". The smallest distance between the IPA and the prostate capsule and membranous urethral length (MUL), serving as a surrogate for distance between corporal tissue and prostatic apex, were also measured. These MRI findings were compared between prostate volumes >40 and <40 ml and between MRI/pathological features of the dominant intraprostatic lesion. A total of 160 men (median age 70 years, interquartile range [IQR] 64-76) were included. The most common NVB pattern was "classic" (80.0-85.0%), followed by the "adherent" NVB pattern (13.8-18.1%). The median smallest distance between the IPA and prostate was 2.3 cm (IQR 1.8-2.8 cm), with 3.1-3.8% less than 1.0 cm. The median MUL was 1.5 cm (IQR, 1.2-1.8 cm), with 2.5% of patients less than 1.0 cm. No significant association was found between these MRI features and prostate volume or other variables (p = 0.09-0.99). In conclusion, most PCa patients demonstrated favorable anatomy for potential dose sparing of critical structures. Comprehensive MRI illustrations are provided to help radiation oncologists recognize the location, trajectory, and relationship of these structures, facilitating their contouring and ultimately aiding in achieving meaningful dose reductions to these erectile function structures.

Prostate MRI has transformed lesion detection and risk stratification in prostate cancer, but its impact is constrained by the high cost of the exam, variability in interpretation, and limited scalability. False negatives, false positives, and moderate inter-reader agreement undermine reliability, while long acquisition times restrict throughput. Artificial intelligence (AI) offers potential solutions to address many of the limitations of prostate MRI in the clinical management pathway. Machine learning-based triage can refine patient selection to optimize resources. Deep learning reconstruction enables accelerated acquisition while preserving diagnostic quality, with multiple FDA-cleared products now in clinical use. Ongoing development of automated quality assessment and artifact correction aims to improve reliability by reducing nondiagnostic exams. In image interpretation, AI models for lesion detection and clinically significant prostate cancer prediction achieve performance comparable to radiologists, and the PI-CAI international reader study has provided the strongest evidence to date of non-inferiority at scale. More recent work extends MRI-derived features into prognostic modeling of recurrence, metastasis, and functional outcomes. This review synthesizes progress across five domains-triage, accelerated acquisition and reconstruction, image quality assurance, diagnosis, and prognosis-highlighting the level of evidence, validation status, and barriers to adoption. While acquisition and reconstruction are furthest along, with FDA-cleared tools and prospective evaluations, triage, quality control, and prognosis remain earlier in development. Ensuring equitable performance across populations, incorporating uncertainty estimation, and conducting prospective workflow trials will be essential to move from promising prototypes to routine practice. Ultimately, AI could accelerate the adoption of prostate MRI toward a scalable platform for earlier detection and population-level prostate cancer management. EVIDENCE LEVEL: N/A TECHNICAL EFFICACY: 3.

BACKGROUND:Access to prostate MRI remains limited due to resource constraints and the need for expert interpretation. PURPOSE/OBJECTIVE:To develop machine learning (ML) models that enable risk-based triage for prostate MRI (ProMT-ML) in the evaluation of prostate cancer. STUDY TYPE/METHODS:Retrospective and prospective. POPULATION/METHODS:A total of 11,879 retrospective MRI scans for suspected prostate cancer from a multi-hospital health system, divided into training (N = 9504) and test (N = 2375) sets. A total of 4551 records for prospective validation. FIELD STRENGTH/SEQUENCE/UNASSIGNED:1.5T and 3T/Turbo-spin echo T2-weighted imaging (T2WI), diffusion-weighted imaging (DWI), and dynamic contrast-enhanced (DCE). ASSESSMENT/RESULTS:Prostate Imaging Reporting and Data System (PI-RADS) scores were retrieved from MRI reports. The Boruta algorithm was used to select final input features from candidate features. Two models were developed using supervised ML to estimate the likelihood of an abnormal MRI, defined as PI-RADS ≥ 3: Model A (with prostate volume) and Model B (without prostate volume). Models were compared to PSA. Prostate biopsy pathology was assessed to evaluate potential clinical impact. STATISTICAL TESTS/METHODS:Area under the receiver operating characteristic curve (AUC) was the primary performance metric. RESULTS:A total of 5580 (46.9%) subjects had a PI-RADS score ≥ 3. After feature selection, Model A included age, PSA, body mass index, and prostate volume, while Model B included age, PSA, body mass index, and systolic blood pressure. Both models A (AUC 0.711) and B (AUC 0.616) significantly outperformed PSA (AUC 0.593). Compared to PSA threshold > 4 ng/mL, Model A demonstrated significantly improved specificity (28.3% vs. 21.9%) and no significant difference in sensitivity (89.0% vs. 86.7%). Among false negatives (Model A: 8.0% (62/776); Model B: 16.8% (130/776)), most (Model A: 87%; Model B: 69%) had benign or clinically insignificant disease on biopsy. On prospective validation, both versions of ProMT-ML significantly outperformed PSA. DATA CONCLUSION/CONCLUSIONS:ProMT-ML provides personalized risk estimates of abnormal prostate MRI and can support triage of this test. TECHNICAL EFFICACY/UNASSIGNED:Stage 4.

Various imaging modalities play key roles throughout the different stages of prostate cancer. Each imaging modality has different strengths and weaknesses and various scoring systems or frameworks are used to interpret their findings. Discordances between imaging modalities or interpretation frameworks, and even with clinicopathological findings are not uncommon. Discordances often lead to challenges in the decision-making process, especially with dynamically changing indications for newer imaging modalities. While more research is needed on harmonizing interpretations across different modalities, multidisciplinary team discussion is key to optimizing management of patients with prostate cancer when such discordances are present. In this comprehensive review, we take a deep dive in to these various discordances seen in clinical practice and explore their clinical implications.

PURPOSE/OBJECTIVE:To explore pragmatic approaches integrating MRI and PSMA-PET/CT for evaluating extraprostatic extension (EPE) of prostate cancer (PCa). METHODS:>12). Diagnostic performance was tested with receiver operating characteristic (ROC) curves and compared using DeLong and McNemar tests. RESULTS:>12 among which 87.5% (7/8) were corrected upgraded and had pathological EPE. CONCLUSION/CONCLUSIONS:Several pragmatic approaches were explored for integrating MRI and PSMA-PET/CT to assess EPE in PCa. Combining morphological information from MRI and PSMA expression on PET/CT demonstrated good diagnostic performance and may be a simple pragmatic integrated method that can be used.

PURPOSE/OBJECTIVE:Both MRI and PSMA PET/CT are often utilized for staging of intermediate-risk (IR) and high-risk (HR) prostate cancer (PCa). Recent studies found that PSMA PET/CT was superior to MRI in correctly identifying final pathological tumor stage, dominant nodule, extraprostatic extension (EPE), and small areas of clinically significant PCa. We sought to determine if PSMA PET/CT was superior to MRI in both locoregional staging of PCa and potential focal therapy planning. METHODS:We retrospectively analyzed our prospectively collected, IRB-approved database of all patients who underwent prostatectomy at one institution between 10/1/2019-2/29/2024. Patients were excluded if they did not pre-operatively undergo both MRI and PSMA PET/CT. 2 × 2 tables were used to compare each modality to the "gold standard" of prostatectomy specimen in both the proper detection of laterality and presence of EPE. Sensitivities and specificities were compared using a chi-squared test. HR v. IR groups were compared using a Wilcoxon rank sum test for continuous variables and Fisher's exact test for categorical variables. Results were considered significant at p < 0.05. RESULTS:580 patients underwent prostatectomy within the specified timeframe. 78 patients met inclusion criteria. MRI was more sensitive in the detection of EPE than PSMA PET/CT (23.5% v. 7.8%, p = 0.0294). MRI and PSMA PET/CT were similar in the specificity of EPE detection. In the identification of laterality, MRI was more specific (86.7% v. 56.7%, p = 0.0099), while sensitivities were similar between the modalities. CONCLUSIONS:MRI was superior to PSMA PET/CT in the proper detection of both EPE and laterality in patients with IR and HR PCa.

Endometriosis is a common disease, affecting approximately 10% of women of reproductive age. Several intersecting guidelines and consensus statements provide information on imaging diagnosis and surveillance strategies for endometriomas. SRU consensus panel recommendations provide information on initial detection of endometriosis on routine pelvic imaging. Revised American Society of Reproductive Medicine (rASRM) classification, the #ENZIAN classification, and the deep pelvic endometriosis index (dPEI) aim to assess the overall extent of disease and assist in presurgical planning. The Ovarian-Adnexal Reporting and Data System (O-RADS) aims to risk stratify lesions evaluated with US or MR based on their imaging morphology, from typical benign lesions to atypical presentations and malignant transformation. Emerging data shows increased risk of ovarian cancer in patients with endometriosis, especially following menopause and in those patients with long standing endometriosis. (Chen et al. in Front Oncol. 14:1329133, 2024;Streuli et al. in Climacteric. 20:138-143, 2017;Secosan et al. in Diagnostics (Basel). 10:134, 2020;Inceboz in Womens Health (Lond Engl). 11:711-715, 2015;Cassani et al. in Maturitas. 190, 2024;Gemmell et al. in Hum Reprod Update. 23:481-500, 2017;Giannella et al. in Cancers (Basel). 13:4026, 2021;) Current O-RADS guidelines mandate follow-up of endometriomas up to 2 years with further follow-up based on clinical factors. No consensus guidelines exist for imaging surveillance of patients with deep endometriosis from a malignancy standpoint. This review explores the imaging appearance of endometriomas, imaging features of malignant transformation, surveillance strategies and gaps in current literature, and attempts to better understand the risk of malignancy and to encourage further research for long-term imaging surveillance of endometriosis patients.