Purpose:We sought to determine whether combining prostate health index (Phi) with urinary prostate cancer antigen 3 (PCA3) and TMPRSS2:ERG (T2:ERG) could improve selection of men for prostate biopsy. These biomarkers have been validated in prostate cancer (PCa) detection separately, but their combination has not previously been developed. Materials and Methods:Prebiopsy blood and post-digital rectal examination urine specimens were assayed to predict subsequent biopsy outcomes from training and validation cohorts (1073 participants across 11 academic centers). Clinical algorithms for combining Phi and PCA3-T2:ERG to predict Grade Group ≥ 2 (GG ≥ 2) PCa were formulated using the training cohort (N = 512). Prediction rules and hypotheses were locked before validation using biopsy-naïve men from the NCI Early Detection Research Network urinary PCA3 trial (N = 561). Rules were compared in weighted sum of specificity and sensitivity with weights specified a priori, and P values were obtained through bootstrap in the validation study. Results:Primary validation analysis showed that Phi combined with urinary PCA3 outperformed Phi alone (P = .002). Furthermore, serum Phi combined with urinary PCA3-T2:ERG outperformed urinary PCA3-T2:ERG in each of the 3 algorithms reflecting different potential clinical workflows: (1) serum Phi and urine PCA3-T2:ERG tested simultaneously, either exceeding its own threshold (P = .04); (2) urine PCA3-T2:ERG first and those in the grey zone resolved by subsequent serum Phi (P = .03); and (3) serum Phi first and those in the grey zone resolved by subsequent urine PCA3-T2:ERG (P = .002). Conclusions:Combining serum Phi with urinary PCA3 RNA alone or together with urinary T2:ERG RNA, simultaneously or sequentially, improves selection of men for initial prostate biopsy and represents an avenue to improve early detection of aggressive PCa.

Introduction Prostate cancer (PCa) extraprostatic extension (EPE) is common, occurring in ∼30% of men undergoing radical prostatectomy; positive surgical margins (PSM) during radical prostatectomy are frequent as well, occurring in up to 50% of cases with EPE. Stimulated Raman histology (SRH) is a novel microscopic technique allowing real time, label-free, high-resolution microscopic images of unprocessed, un-sectioned tissue providing both morphologic and biochemical information of imaged tissue. We hypothesized that artificial intelligence (AI) utilizing prostate biopsy SRH from MRI identified PCa and clinical variables can predict both EPE and PSM. Methods Prospectively, targeted prostate biopsies from ex-vivo radical prostatectomy specimens, 99% obtained from MRI visible PCa. Prostate biopsies were scanned in a SRH microscope using two Raman shifts: 2845cm-1 and 2930cm-1, to create SRH images. Ex-vivo prostate biopsies were taken from 108 radical prostatectomy specimens to train an attention-based multiple instance learning deep learning neural network (DLNN) made interpretations based on the highest risk patch(s) and/or clinical characteristics as described in Table 1. The DLNN was tested on prostate biopsies from 33 consecutive radical prostatectomy specimens MRI identified PCa (Table 1) yielding receiver operating curve area under the curve (AUC) characteristics. Clinical variables utilized for creation and testing of the DLNN were age, PSA, PSA density, PI-RADS score, region of interest largest dimension and region of interest area. Results The combination of clinical variables and SRH image resulted in an AUC for prediction of PSM of 0.875. Utilizing clinical variables alone, the DLNN predicted PSM with an AUC of 0.787, while assessment of the SRH alone showed an AUC of 0.741.Prediction based on SRH alone, the DLNN predicted EPE with an AUC of 0.795; while analyzing clinical variables alone showed an AUC of 0.59. The addition of clinical variables the SRH DLNN did not further increase the AUC above that of SRH image assessment alone. Conclusions The prediction of EPE alone was best with AI and SRH alone, indicating the clinical factors were not necessary for prediction of EPE. However, the prediction of PSM was best with both clinical and SRH, indicating clinical variables may be required to accurately predict cases where surgeons are likely to perform an incomplete resection. Further validation of these results is required in larger multicentre studies.

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. 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. 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

Background In-field or in-margin recurrence after partial gland cryosurgical ablation (PGCA) of prostate cancer (PCa) remains a limitation of the paradigm. Stimulated Raman histology (SRH) is a novel microscopic technique allowing real time, label-free, high-resolution microscopic images of unprocessed, un-sectioned tissue which can be interpreted by humans or artificial intelligence (AI). We evaluated surgical team and AI interpretation of SRH for real-time pathologic feedback in the planning and treatment of PCa with PGCA. Methods About 12 participants underwent prostate mapping biopsies during PGCA of their PCa between January and June 2022. Prostate biopsies were immediately scanned in a SRH microscope at 20 microns depth using 2 Raman shifts to create SRH images which were interpreted by the surgical team intraoperatively to guide PGCA, and retrospectively assessed by AI. The cores were then processed, hematoxylin and eosin stained as per normal pathologic protocols and used for ground truth pathologic assessment. Results Surgical team interpretation of SRH intraoperatively revealed 98.1% accuracy, 100% sensitivity, 97.3% specificity for identification of PCa, while AI showed a 97.9% accuracy, 100% sensitivity and 97.5% specificity for identification of clinically significant PCa. 3 participants’ PGCA treatments were modified after SRH visualized PCa adjacent to an expected MRI predicted tumor margin or at an untreated cryosurgical margin. Conclusion SRH allows for accurate rapid identification of PCa in PB by a surgical team interpretation or AI. PCa tumor mapping and margin assessment during PGCA appears to be feasible and accurate. Further studies evaluating impact on clinical outcomes are warranted.

BACKGROUND:Access to prostate MRI remains limited due to resource constraints and the need for expert interpretation. PURPOSE:To develop machine learning (ML) models that enable risk-based triage for prostate MRI (ProMT-ML) in the evaluation of prostate cancer. STUDY TYPE:Retrospective and prospective. POPULATION: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:1.5T and 3T/Turbo-spin echo T2-weighted imaging (T2WI), diffusion-weighted imaging (DWI), and dynamic contrast-enhanced (DCE). ASSESSMENT: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: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:ProMT-ML provides personalized risk estimates of abnormal prostate MRI and can support triage of this test. LEVEL OF EVIDENCE: 2: TECHNICAL EFFICACY:Stage 4.

Background:Minimally invasive focal therapy of low- to intermediate-risk prostate cancer is becoming more common and has demonstrated lower morbidity compared to other treatments. Multiparametric prostate magnetic resonance imaging (mpMRI) has the potential to be an effective posttreatment evaluation method for residual/recurrent neoplasm.Objective:This study aimed to evaluate the ability of mpMRI to detect residual/recurrent neoplasm after focal therapy treatment of prostate cancer using a 3-point Likert scale.Methods:This retrospective study included patients who underwent focal therapy utilizing cryoablation, high-frequency ultrasound, and radiofrequency ablation for low- to intermediate-risk prostate cancer with baseline mpMRI and biopsy and a 6- to 12-month follow-up mpMRI and biopsy. Three abdominal fellowship-trained readers were asked to evaluate the follow-up mpMRI utilizing a 3-point Likert scale based on the level of suspicion as "nonviable," "equivocal," or "viable." Diagnostic statistics and Light's kappa for interreader variability were calculated.Results:A total of 142 patients were included (mean age, 65 +/- 7 years). When considering "equivocal" or "viable" as positive, the overall sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and area under the receiver operating characteristic curve (AUC) for detecting recurrent grade group (GG) 2 or greater disease for Reader 1 were 0.47, 0.83, 0.24, 0.93, and 0.65; for Reader 2, 0.73, 0.75, 0.26, 0.96, and 0.74; and for Reader 3, 0.73, 0.57, 0.17, 0.95, and 0.65. When considering "viable" as positive, the overall sensitivity, specificity, PPV, NPV, and AUC for Reader 1 were 0.47, 0.92, 0.41, 0.94, and 0.69; for Reader 2, 0.33, 0.97, 0.56, 0.93, and 0.65; and for Reader 3, 0.53, 0.84, 0.29, 0.94, and 0.69. kappa was 0.39.Conclusions:This study suggests that DCE and DWI are the most important sequences in mpMRI and demonstrates the efficacy of utilizing a 3-point grading system in detecting and diagnosing prostate cancer after focal therapy.Clinical Impact:mpMRI can be used to monitor for residual/recurrent disease after focal therapy.

Introduction The initial cores during an MRI-targeted prostate biopsy have demonstrated higher diagnostic value compared to additional cores for the identification of prostate cancer (PCa). Stimulated Raman histology (SRH) creates histologic images interpretable by humans or artificial intelligence (AI) which can provide near-real time prostate biopsy pathology. This study aimed to evaluate the accuracy of PCa detection with the first MRI targeted prostate biopsy imaged with SRH and interpreted by a published AI algorithm. Methods Men undergoing MRI targeted prostate biopsy for PI-RADS 3-5 region of interest (ROI) were prospectively enrolled in an IRB approved study. Men underwent a cognitive MRI-targeted transperineal prostate biopsy with systematic sampling, utilizing the ExactVu micro-ultrasound. Prostate biopsies were kept fresh before scanning with the SRH microscope; the first 22 consecutive men (24 ROI) biopsied during June and July 2024 were reviewed. A published convolutional neural network developed and tested at NYU Langone Health, without cluster analysis, was incorporated into the SRH microscope to provide a prostate cancer risk score (>15 considered PCa). Following SRH creation, the cores underwent standard pathologic processing and pathologist interpretation as per standard of care, which served as ground truth. The time to SRH diagnosis for clinically significant PCa (csPCa), along with the sensitivity and specificity of PCa detection with AI was calculated; csPCa was defined as ISUP Grade Group 2 (> 5% pattern 4). Results Time for SRH-AI for the first targeted biopsy was 7 minutes, while only 4.75 minutes for additional biopsies. The median time from biopsy to SRH diagnosis was 7 minutes (interquartile range(IQR):0) in patients with PCa and 27 (IQR:10) in patients without PCa. 50% of ROI contained PCa and 37.5% contained csPCa; systematic biopsy did not identify csPCa missed in the ROI. When a MRI target contained csPCa, the first biopsy identified PCa in 100% (9/9) of cases.Utilizing SRH-AI PCa cut off value >15, analyzing all MRI targeted biopsies showed a sensitivity for csPCa detection of 100%, and a specificity of 92%. Utilizing SRH-AI cut off of >20, sensitivity of csPCa detection was 78%, and the specificity was 100%. Figure 1 shows SRH with and without AI annotation. Analysis of both false positive and negative biopsies resulted in classification error due to missing cluster analysis in AI utilized. Conclusions In MRI targeted biopsies with csPCa identified, SRH identified PCa within 7 minutes in the first MRI targeted biopsy. Rapid scan protocols which reduces time for SRH has been accomplished reducing scan time to ∼1 minute but images are not interpretable by humans, therefore more experience with full scan speed is required before deployment. Cluster analysis appears to be required for most accurate biopsy interpretation. Further model training and testing will require before clinical application, though adoption may guide biopsy intensity or allow discussion of PCa diagnosis immediately after the procedure when the ROI contains csPCa.

Purpose:Screening colonoscopies (CS) performed before prostate stereotactic body radiation therapy (SBRT) allow for identifying synchronous malignancies and comorbid gastrointestinal (GI) conditions. Performing these procedures prior to radiation precludes the necessity of post-SBRT pelvic instrumentation, which may lead to severe toxicity and fistulization. We review compliance of CSs, incidence of GI pathology, and the impact of pretreatment CS findings on subsequent physician-reported toxicity and patient-reported quality of life (QoL). Methods and Materials:We reviewed an institutional database of patients treated for prostate cancer with SBRT including toxicity and QoL outcomes. A detailed review of pretreatment CS findings was reviewed including identification of diverticulosis, location of polyp resection, and presence of hemorrhoids. Pretreatment CS findings were then correlated with outcomes following SBRT. Results:Identification of comorbid GI conditions was a common event, with the presence of diverticulosis in 49.5% (n = 100), hemorrhoids in 67% (n = 136), and polyps in 48% (n = 98). More than half of patients with polyps removed had at least 1 removed from the rectosigmoid. Pretreatment CS did not introduce a delay in SBRT start date. Grade 1 toxicity was significantly lower in patients who underwent CS closer to the initiation of SBRT. There was no increased risk of physician-graded toxicity in the presence of diverticulosis, hemorrhoids, or polyps. Patient-reported GI QoL pattern in our screening cohort mimicked that seen in the previously published nonscreened population. There was no overt QoL detriment observed in patients who had GI pathology identified before SBRT. Conclusions:GI pathology identified in our elderly patient population was commonly identified on pretreatment CS. Screening CS may optimize bowel health for patients heading into radiation therapy. Toxicity and QoL for patients with GI pathologies identified on pretreatment CS do not preclude the delivery of prostate SBRT. We advocate for pretreatment CS in patients eligible prior to SBRT.

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.

INTRODUCTION: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: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.

Importance: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:To assess whether biparametric MRI is noninferior to multiparametric MRI for diagnosis of clinically significant prostate cancer. Design, Setting, and Participants: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: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: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: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: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:ClinicalTrials.gov Identifier: NCT04571840.

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. Consecutive patients with prostate cancer that underwent PET/CT with [18F]F-DCFPyL in 2021–2023 for newly diagnosed intermediate-/high-risk prostate cancer or recurrent/metastatic disease and had PSMA-avid rib lesions were included. Imaging findings assessed were: lesion number, PSMA expression (maximum standard uptake value (SUVmax), 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. One hundred and seventy-five men (median 71 years, IQR 65–77) with PSMA-avid rib lesions were included; 47/175 (26.9

To explore pragmatic approaches integrating MRI and PSMA-PET/CT for evaluating extraprostatic extension (EPE) of prostate cancer (PCa). Consecutive patients with newly-diagnosed PCa that underwent multiparametric MRI and PSMA-PET/CT, followed by radical prostatectomy in 2021–2024 were included. Imaging parameters assessed on both modalities were: size, length of capsular contact (LCC), Likert scales (MRI EPE grade/PSMA Likert scale), PI-RADS/PRIMARY scores, and SUVmax. Three pragmatic integrated approaches were tested: (1) Integration of Likert scales (positive if either or both MRI and PSMA-PET/CT were positive); (2) P score (framework combining PI-RADS + PRIMARY); and (3) combining MRI morphological information with PSMA-PET/CT functional information (upgrading suspicion of lesions with LCC below cutoff if SUVmax>12). Diagnostic performance was tested with receiver operating characteristic (ROC) curves and compared using DeLong and McNemar tests. 67 men (median age, 66 years) with EPE in 76.1