Faculty Bibliography

BACKGROUND AND OBJECTIVE/UNASSIGNED:Stimulated Raman histology (SRH) offers promising near-real-time tissue visualization for intraoperative pathology assessment. We present preliminary results from the ROBOSPEC study, with a focus on the accuracy of results obtained via an integrated artificial intelligence (AI) tool. METHODS/UNASSIGNED:ROBOSPEC is a prospective, single-arm pilot study involving patients with prostate cancer undergoing robot-assisted radical prostatectomy (RARP). Probes from the RP specimens from the first 18 patients with intermediate-risk or high-risk prostate cancer were collected bilaterally from the dorsolateral sides of the prostate and examined with frozen section with hematoxylin and eosin staining (cryo-HE), SRH imaging (NIO laser imaging system, Invenio Imaging, Santa Clara, CA, USA). A previously published New York University AI algorithm (NYU-AI) that is based on the Inception-ResNet-v2 CNN architecture was used to generate three-color overlays to assist in interpretation. SRH images were reviewed by blinded urologists using this AI-enhanced output. KEY FINDINGS AND LIMITATIONS/UNASSIGNED: > 0.05). Patient-based analysis yielded sensitivity and a negative predictive value (NPV) of 1.0, specificity of 0.93, and a positive predictive value of 0.75. Sample-based analysis showed similar performance, with specificity of 0.97 and identical sensitivity and NPV. These findings indicate strong diagnostic agreement between NYU-AI and conventional intraoperative pathology. Limitations of the study include the small patient cohort, the single-center design, previous training of the NYU-AI tool on prostate biopsy and periprostatic surgical-bed samples, and the lack of testing of interobserver agreement. CONCLUSIONS AND CLINICAL IMPLICATIONS/UNASSIGNED:Our preliminary findings support the potential of SRH with NYU-AI for intraoperative detection of positive surgical margins during RARP. Implementation of this technique should be further discussed after more studies have been conducted. PATIENT SUMMARY/UNASSIGNED:We looked at an artificial intelligence program using a method called stimulated Raman histology to assess the cancer status of the cutting margin during robot-assisted surgery to remove the prostate. Our preliminary results show that this method could be an alternative to the current standard as it provides accurate and faster results.

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.

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:Partial nephrectomy has been advocated as the preferred surgical approach for small kidney tumors over total nephrectomy. However, partial nephrectomy is associated with increased perioperative risk. Estimating renal function after nephrectomy can facilitate personalized patient counseling, guide surgical approach, and identify patients who could benefit from perioperative interventions. Existing prediction models have several limitations including the lack of external validation or a user-friendly tool or application, and most have used traditional statistical methods. METHODS:We used data from two academic medical institutions and machine learning (ML) methods to develop and externally validate renal function after nephrectomy-machine learning (RFAN-ML), a model to estimate long-term renal function after partial or total nephrectomy. Boruta feature selection was used to select four routinely available clinical features, specifically age, BMI, preoperative renal function, and nephrectomy type. In the training set of 1,932 patients, we compared six ML regression models representing a set of both ensemble and nonensemble ML algorithms and optimized for root mean squared error (RMSE). This model was evaluated in a test set of 1,995 patients, and the best performing model was selected as RFAN-ML. RESULTS:, and mean absolute error. CONCLUSION/CONCLUSIONS:We developed and externally validated RFAN-ML, a ML model to predict renal function after nephrectomy, and have deployed our model online. RFAN-ML has the potential to improve the care and outcomes in patients with kidney tumors by informing personalized patient counseling and guiding surgical planning.

IMPORTANCE/UNASSIGNED:Multiparametric magnetic resonance imaging (MRI), with or without prostate biopsy, has become the standard of care for diagnosing clinically significant prostate cancer. Resource capacity limits widespread adoption. Biparametric MRI, which omits the gadolinium contrast sequence, is a shorter and cheaper alternative offering time-saving capacity gains for health systems globally. OBJECTIVE/UNASSIGNED:To assess whether biparametric MRI is noninferior to multiparametric MRI for diagnosis of clinically significant prostate cancer. DESIGN, SETTING, AND PARTICIPANTS/UNASSIGNED:A prospective, multicenter, within-patient, noninferiority trial of biopsy-naive men from 22 centers (12 countries) with clinical suspicion of prostate cancer (elevated prostate-specific antigen [PSA] level and/or abnormal digital rectal examination findings) from April 2022 to September 2023, with the last follow-up conducted on December 3, 2024. INTERVENTIONS/UNASSIGNED:Participants underwent multiparametric MRI, comprising T2-weighted, diffusion-weighted, and dynamic contrast-enhanced (DCE) sequences. Radiologists reported abbreviated biparametric MRI first (T2-weighted and diffusion-weighted), blinded to the DCE sequence. After unblinding, radiologists reported the full multiparametric MRI. Patients underwent a targeted biopsy with or without systematic biopsy if either biparametric MRI or multiparametric MRI was suggestive of clinically significant prostate cancer. MAIN OUTCOMES AND MEASURES/UNASSIGNED:The primary outcome was the proportion of men with clinically significant prostate cancer. Secondary outcomes included the proportion of men with clinically insignificant cancer. The noninferiority margin was 5%. RESULTS/UNASSIGNED:Of 555 men recruited, 490 were included for primary outcome analysis. Median age was 65 (IQR, 59-70) years and median PSA level was 5.6 (IQR, 4.4-8.0) ng/mL. The proportion of patients with abnormal digital rectal examination findings was 12.7%. Biparametric MRI was noninferior to multiparametric MRI, detecting clinically significant prostate cancer in 143 of 490 men (29.2%), compared with 145 of 490 men (29.6%) (difference, -0.4 [95% CI, -1.2 to 0.4] percentage points; P = .50). Biparametric MRI detected clinically insignificant cancer in 45 of 490 men (9.2%), compared with 47 of 490 men (9.6%) with the use of multiparametric MRI (difference, -0.4 [95% CI, -1.2 to 0.4] percentage points). Central quality control demonstrated that 99% of scans were of adequate diagnostic quality. CONCLUSION AND RELEVANCE/UNASSIGNED:In men with suspected prostate cancer, provided image quality is adequate, an abbreviated biparametric MRI scan, with or without targeted biopsy, could become the new standard of care for prostate cancer diagnosis. With approximately 4 million prostate MRIs performed globally annually, adopting biparametric MRI could substantially increase scanner throughput and reduce costs worldwide. TRIAL REGISTRATION/UNASSIGNED:ClinicalTrials.gov Identifier: NCT04571840.

OBJECTIVES/OBJECTIVE:Prostate-specific membrane antigen (PSMA)-PET/CT has become integral to management of prostate cancer; however, PSMA-avid rib lesions pose a diagnostic challenge. This study investigated clinicopathological and imaging findings that predict metastatic etiology of PSMA-avid rib lesions. MATERIALS AND METHODS/METHODS:), miPSMA score), CT features (sclerotic, lucent, fracture, no correlate), other sites of metastases, and primary tumor findings. A composite reference standard for rib lesion etiology (metastatic vs non-metastatic) based on histopathology, serial imaging, and clinical assessment was used. RESULTS:, miPSMA), more commonly involved multiple ribs, and were more often sclerotic (p < 0.01); lucency/fractures were only seen in benign lesions. CONCLUSION/CONCLUSIONS:Several imaging and clinicopathological factors differed between PSMA-avid metastatic and benign lesions. Isolated rib lesions without other sites of metastasis are almost always benign. Careful assessment of CT features can help diagnose benign lesions. KEY POINTS/CONCLUSIONS:Question While prostate-specific membrane antigen (PSMA)-PET/CT has become integral to the management of prostate cancer, PSMA-avid rib lesions pose a diagnostic challenge. Findings Approximately a quarter of patients who had PSMA-avid rib lesions were metastatic. However, only 2.1% of them had isolated rib metastasis (without PSMA-avid metastases elsewhere). Clinical relevance Isolated PSMA-avid rib lesions are almost always benign when there is no evidence of metastatic disease elsewhere. Scrutinizing CT features can help diagnose benign PSMA-avid lesions with greater certainty.

BACKGROUND:Despite the evidence supporting the use of focal therapy (FT) in patients with localized prostate cancer (PCa), considerable variability exists in the patient selection criteria across current studies. This study aims to review the most recent evidence concerning the optimal approach to patient selection for FT in PCa. METHODS:PubMed database was systematically queried for studies reporting patient selection criteria in FT for PCa before December 31, 2023. After excluding non-relevant articles and a quality assessment, data were extracted, and results were described qualitatively. RESULTS:There is no level I evidence regarding the best patient selection approach for FT in patients with PCa. Current international multidisciplinary consensus statements recommend multiparametric magnetic resonance imaging (mpMRI) followed by MRI-targeted and systematic biopsy for all candidates. FT may be considered in clinically localized, intermediate risk (Gleason 3 + 4 and 4 + 3), and preferably unifocal disease. Patients should have an acceptable life expectancy. Those with prostate volume >50 ml and erectile dysfunction should not be excluded from FT. Prostate-specific antigen (PSA) level of < 20 (ideally < 10) ng/mL is recommended. However, the utility of other molecular and genomic biomarkers in patient selection for FT remains unknown. CONCLUSIONS:FT may be considered in well-selected patients with localized PCa. This review provides a comprehensive insight regarding the optimal approach for patient selection in FT.

INTRODUCTION/BACKGROUND:High-volume (≥ 50 %) biopsy core involvement (HVCI) is an independent risk factor for unfavorable intermediate-risk prostate cancer by NCCN guidelines. The studies demonstrating increased recurrence in high-volume disease were conducted in an era of conventional fractionation, often without dose-escalation. In the SBRT era, we explore the value of this pathologic criteria in intermediate-risk disease. METHODS:A large institutional database was reviewed to identify patients diagnosed with localized intermediate-risk (Gleason Grade [GG] 2 and 3) disease, who were treated with definitive five-fraction SBRT without ADT. HVCI was analyzed (1) traditionally with all positive cores given equal weight as well as weighted with a positive core of GG1 to GG3 given (2) linearly and (3) exponentially increased weight. Oncologic outcomes were analyzed using Cox and linear regression analysis. RESULTS:From 2009 to 2018, 888 patients with intermediate-risk prostate cancer were treated with five-fraction SBRT monotherapy to a median dose of 3500 cGy. The majority (68 %) had GG2 disease. HVCI was present in the 22 % and was inversely related to prostate volume and directly related to T-stage. Biochemical disease-free survival (BDFS) was not significantly associated with HVCI in the cohort (p = 0.47) nor in the GG2 (p = 0.85) and GG3 (p = 0.26) sub-cohorts. Similarly, when linear or exponential weight was given to a core with higher-grade disease, there was no association with BDFS. Finally, PSA nadir was not associated with HVCI; however, time to PSA nadir (TTN) was negatively associated with HVCI in the GG3 sub-cohort (p = 0.04). CONCLUSION/CONCLUSIONS:With a median follow-up of 4.1 years, HVCI was not associated with BDFS following SBRT monotherapy, particularly in patients with otherwise favorable intermediate-risk disease (GG2). TTN analysis suggests that HVCI may remain prognostic in GG3 disease (by definition unfavorable intermediate-risk). Further work should prospectively confirm whether HVCI is unnecessary in risk-stratifying GG2 disease in the SBRT era.

OBJECTIVES/OBJECTIVE:An increasing number of patients with prostate cancer (PCa) undergo assessment with magnetic resonance imaging (MRI) and prostate-specific membrane antigen positron emission tomography/computed tomography (PSMA-PET/CT). This offers comprehensive multimodality staging but can lead to discrepancies. The objective was to assess the rates and types of discordance between MRI and PSMA-PET/CT for primary PCa assessment. MATERIALS AND METHODS/METHODS:Consecutive men diagnosed with intermediate and high-risk PCa who underwent MRI and PSMA-PET/CT in 2021-2023 were retrospectively included. MRI and PSMA-PET/CT were interpreted using PI-RADS v2.1 and PRIMARY scores. Discordances between the two imaging modalities were categorized as "minor" (larger or additional lesion seen on one modality) or "major" (positive on only one modality or different index lesions between MRI and PSMA-PET/CT) and reconciled using radical prostatectomy or biopsy specimens. RESULTS:Three hundred and nine men (median age 69 years, interquartile range (IQR) 64-75) were included. Most had Gleason Grade Group ≥ 3 PCa (70.9% (219/309)). Median PSA was 9.0 ng/mL (IQR 5.6-13.6). MRI and PSMA-PET/CT were concordant in 157/309 (50.8%) and discordant in 152/309 (49.1%) patients; with 39/152 (25.7%) major and 113/152 (74.3%) minor discordances. Of 27 patients with lesions only seen on MRI, 85.2% (23/27) were clinically significant PCa (csPCa). Of 23 patients with lesions only seen on PSMA-PET/CT, 78.3% (18/23) were csPCa. Altogether, lesions seen on only one modality were csPCa in 80.0% (36/45). CONCLUSION/CONCLUSIONS:MRI and PSMA-PET/CT were discordant in half of patients for primary PCa evaluation, with major discrepancies seen in roughly one out of eight patients. KEY POINTS/CONCLUSIONS:Question While both MRI and PSMA-PET/CT can be used for primary tumor assessment, the discordances between them are not well established. Findings MRI and PSMA-PET/CT were discordant in about half of the patients. Most prostate lesions seen on only one modality were significant cancer. Clinical relevance MRI and PSMA-PET/CT are often discordant for assessing the primary prostate tumor. Using both modalities for primary prostate tumor evaluation can provide complementary information that may substantially impact treatment planning.

Prostate cancer remains one of the most prevalent malignancies affecting men worldwide, making early detection and advancements in precision medicine crucial for effective intervention and treatment. A standardized protocol is presented for utilizing stimulated Raman histology (SRH) with integrated artificial intelligence (AI) in prostate cancer detection, offering significant advancements over conventional histopathological methods. SRH provides these advancements by enhancing efficiency through near-real-time, label-free imaging of fresh, unstained tissues, thereby eliminating the delays associated with traditional biopsy analysis. By using stimulated Raman scattering (SRS) microscopy to detect the specific vibrational frequencies of CH2 bonds associated with lipids and CH3 bonds linked to proteins and DNA, cancerous and benign tissues in prostate biopsies can be differentiated. The AI model further enhances diagnostic precision, achieving 98.6% accuracy in identifying prostate cancer. The protocol outlines essential steps for sample preparation, imaging, and data analysis, facilitating improved biobanking processes and enabling downstream applications, such as transcriptomics and xenograft studies. This approach accelerates the diagnostic workflow and shows promise for intraoperative applications, potentially aiding surgeons in identifying positive margins intraoperatively. Additionally, the ability to re-scan and adjust cancer-to-tissue ratios allows for a more tailored analysis of biopsy samples, enhancing tumor detection in unprocessed tissues. Further research and validation are necessary for the widespread adoption of SRH in clinical practice.