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Introduction: Balancing surgical margins and functional outcomes is crucial during radical prostatectomy for prostate cancer. Stimulated Raman Histology (SRH) is a novel, real-time imaging technique that provides histologic images of fresh, unprocessed, and unstained tissue within minutes, which can be interpreted by either humans or artificial intelligence., Methods: Twenty-two participants underwent robotic-assisted laparoscopic radical prostatectomy (RALP) with intraoperative SRH surgical bed assessment. Surgeons resected and imaged surgical bed tissue using SRH and adjusted treatment accordingly. An SRH convolutional neural network (CNN) was developed and tested on 10 consecutive participants. The accuracy, sensitivity, and specificity of the surgical team's interpretation were compared to final histopathological assessment., Results: A total of 121 SRH periprostatic surgical bed tissue (PSBT) assessments were conducted, an average of 5.5 per participant. The accuracy of the surgical team's SRH interpretation of resected PSBT samples was 98%, with 83% sensitivity, and 99% specificity. Intraoperative SRH assessment identified 43% of participants with a pathologic positive surgical margin intraoperatively. PSBT assessment using the CNN demonstrated no overlap in tumor probability prediction between benign and tumor infiltrated samples, mean 0.30% (IQR 0.10-0.43%) and 26% (IQR 18-34%, p<0.005), respectively., Conclusion: SRH demonstrates potential as a valuable tool for real-time intraoperative assessment of surgical margins during RALP. This technique may improve nerve-sparing surgery and facilitate decision-making for further resection, reducing the risk of positive surgical margins and minimizing the risk of recurrence. Further studies with larger cohorts and longer follow-up periods are warranted to confirm the benefits of SRH in RALP.

Purpose: Postoperative prognostic tools allow for improved prediction of future recurrence risk, patient counseling, and assessment of eligibility for adjuvant treatments and ensure appropriate follow-up surveillance. The purpose of this analysis was to validate existing prognostic models for patients with kidney cancer., Materials and Methods: The Canadian Kidney Cancer information system is a prospective cohort of patients managed at 14 institutions since January 1, 2011, to present. The Canadian Kidney Cancer information system was used to assess 15 predictive models for kidney cancer recurrence, 6 for cancer-specific mortality, and 4 for all-cause mortality in patients with a solitary, nonmetastatic kidney tumor treated with surgery (partial or radical nephrectomy). Discrimination was measured using c-statistics, 5-year calibration plots for calibration, and decision curve analysis at 5 years after surgery for net benefit when considering adjuvant therapy., Results: Seven thousand one hundred seventy-four patients were included. For kidney cancer recurrence, c-statistics ranged from 0.62 to 0.83, depending on whether the model was derived and applied to all patients without further stratification, specific risk groups, or specific histological subtypes. Cancer-specific mortality models had c-statistics ranging from 0.60 to 0.89 and all-cause mortality models from 0.60 to 0.73. Using decision curve analysis in patients with clear-cell renal cell carcinoma, the best models for choosing adjuvant therapy to prevent recurrence and cancer-related death were the Mayo Clinic prediction models., Conclusions: Model performance varied considerably with some suitable for clinical use. If using prediction models to select adjuvant therapy, the Mayo Clinic models were best when applied to a large contemporary cohort of Canadian patients.

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., 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., (Copyright © 2024 The Authors. Published by Elsevier Inc. All rights reserved.)

Purpose: Stimulated Raman histology is an innovative technology that generates real-time, high-resolution microscopic images of unprocessed tissue, significantly reducing prostate biopsy interpretation time. This study aims to evaluate the ability for an artificial intelligence convolutional neural network to interpretate prostate biopsy histologic images created with stimulated Raman histology., Materials and Methods: Unprocessed, unlabeled prostate biopsies were prospectively imaged using a stimulated Raman histology microscope. Following stimulated Raman histology creation, the cores underwent standard pathological processing and interpretation by at least 2 genitourinary pathologists to establish a ground truth assessment. A network, trained on 303 prostate biopsies from 100 participants, was used to measure the accuracy, sensitivity, and specificity of detecting prostate cancer on stimulated Raman histology relative to conventional pathology. The performance of the artificial intelligence was evaluated on an independent 113-biopsy test set., Results: Prostate biopsy images obtained through stimulated Raman histology can be generated within a time frame of 2 to 2.75 minutes. The artificial intelligence system achieved a rapid classification of prostate biopsies with cancer, with a potential identification time of approximately 1 minute. The artificial intelligence demonstrated an impressive accuracy of 96.5% in detecting prostate cancer. Moreover, the artificial intelligence exhibited a sensitivity of 96.3% and a specificity of 96.6%., Conclusions: Stimulated Raman histology generates microscopic images capable of accurately identifying prostate cancer in real time, without the need for sectioning or tissue processing. These images can be interpreted by artificial intelligence, providing physicians with near-real-time pathological feedback during the diagnosis or treatment of prostate cancer.

Introduction: Delay between targeted prostate biopsy (PB) and pathologic diagnosis can lead to a concern of inadequate sampling and repeated biopsy. Stimulated Raman histology (SRH) is a novel microscopic technique allowing real-time, label-free, high-resolution microscopic images of unprocessed, unsectioned tissue. This technology holds potential to decrease the time for PB diagnosis from days to minutes. We evaluated the concordance of pathologist interpretation of PB SRH as compared with traditional hematoxylin and eosin (H&E) stained slides., Methods: Men undergoing prostatectomy were included in an IRB-approved prospective study. Ex vivo 18-gauge PB cores, taken from prostatectomy specimen, were scanned in an SRH microscope (NIO; Invenio Imaging) at 20 microns depth using two Raman shifts: 2845 and 2930 cm -1 , to create SRH images. The cores were then processed as per normal pathologic protocols. Sixteen PB containing a mix of benign and malignant histology were used as an SRH training cohort for four genitourinary pathologists, who were then tested on a set of 32 PBs imaged by SRH and processed by traditional H&E. Sensitivity, specificity, accuracy, and concordance for prostate cancer (PCa) detection on SRH relative to H&E were assessed., Results: The mean pathologist accuracy for the identification of any PCa on PB SRH was 95.7%. In identifying any PCa or ISUP grade group 2-5 PCa, a pathologist was independently able to achieve good and very good concordance (κ: 0.769 and 0.845, respectively; p < 0.001). After individual assessment was completed a pathology consensus conference was held for the interpretation of the PB SRH; after the consensus conference the pathologists' concordance in identifying any PCa was also very good (κ: 0.925, p < 0.001; sensitivity 95.6%; specificity 100%)., Conclusion: SRH produces high-quality microscopic images that allow for accurate identification of PCa in real-time without need for sectioning or tissue processing. The pathologist performance improved through progressive training, showing that ultimately high accuracy can be obtained. Ongoing SRH evaluation in the diagnostic and treatment setting hold promise to reduce time to tissue diagnosis, while interpretation by convolutional neural network may further improve diagnostic characteristics and broaden use., (© 2023 The Authors. The Prostate published by Wiley Periodicals LLC.)

Introduction: Renal tumor biopsy requires adequate tissue sampling to aid in the investigation of small renal masses. In some centers the contemporary nondiagnostic renal mass biopsy rate may be as high as 22% and may be as high as 42% in challenging cases. Stimulated Raman Histology (SRH) is a novel microscopic technique which has created the possibility for rapid, label-free, high-resolution images of unprocessed tissue which may be viewed on standard radiology viewing platforms. The application of SRH to renal biopsy may provide the benefits of routine pathologic evaluation during the procedure, thereby reducing nondiagnostic results. We conducted a pilot feasibility study, to assess if renal cell carcinoma (RCC) subtypes may be imaged and to see if high-quality hematoxylin and eosin (H&E) could subsequently be generated., Methods/materials: An 18-gauge core needle biopsy was taken from a series of 25 ex vivo radical or partial nephrectomy specimens. Histologic images of the fresh, unstained biopsy samples were obtained using a SRH microscope using 2 Raman shifts: 2,845 cm -1  and 2,930 cm -1 . The cores were then processed as per routine pathologic protocols. The SRH images and hematoxylin and eosin (H&E) slides were then viewed by a genitourinary pathologist., Results: The SRH microscope took 8 to 11 minutes to produce high-quality images of the renal biopsies. Total of 25 renal tumors including 1 oncocytoma, 3 chromophobe RCC, 16 clear cells RCC, 4 papillary RCC, and 1 medullary RCC were included. All renal tumor subtypes were captured, and the SRH images were easily differentiated from adjacent normal renal parenchyma. High quality H&E slides were produced from each of the renal biopsies after SRH was completed. Immunostains were performed on selected cases and the staining was not affected by the SRH image process., Conclusion: SRH produces high quality images of all renal cell subtypes that can be rapidly produced and easily interpreted to determine renal mass biopsy adequacy, and on occasion, may allow renal tumor subtype identification. Renal biopsies remained available to produce high quality H&E slides and immunostains for confirmation of diagnosis. Procedural application has promise to decrease the known rate of renal mass nondiagnostic biopsies, and application of convolutional neural network methodology may further improve diagnostic capability and increase utilization of renal mass biopsy among urologists., (Copyright © 2023 Elsevier Inc. All rights reserved.)

Background: Patient factors associated with urinary tract cancer can be used to risk stratify patients referred with haematuria, prioritising those with a higher risk of cancer for prompt investigation., Objective: To develop a prediction model for urinary tract cancer in patients referred with haematuria., Design, Setting, and Participants: A prospective observational study was conducted in 10 282 patients from 110 hospitals across 26 countries, aged ≥16 yr and referred to secondary care with haematuria. Patients with a known or previous urological malignancy were excluded., Outcome Measurements and Statistical Analysis: The primary outcomes were the presence or absence of urinary tract cancer (bladder cancer, upper tract urothelial cancer [UTUC], and renal cancer). Mixed-effect multivariable logistic regression was performed with site and country as random effects and clinically important patient-level candidate predictors, chosen a priori, as fixed effects. Predictors were selected primarily using clinical reasoning, in addition to backward stepwise selection. Calibration and discrimination were calculated, and bootstrap validation was performed to calculate optimism., Results and Limitations: The unadjusted prevalence was 17.2% (n = 1763) for bladder cancer, 1.20% (n = 123) for UTUC, and 1.00% (n = 103) for renal cancer. The final model included predictors of increased risk (visible haematuria, age, smoking history, male sex, and family history) and reduced risk (previous haematuria investigations, urinary tract infection, dysuria/suprapubic pain, anticoagulation, catheter use, and previous pelvic radiotherapy). The area under the receiver operating characteristic curve of the final model was 0.86 (95% confidence interval 0.85-0.87). The model is limited to patients without previous urological malignancy., Conclusions: This cancer prediction model is the first to consider established and novel urinary tract cancer diagnostic markers. It can be used in secondary care for risk stratifying patients and aid the clinician's decision-making process in prioritising patients for investigation., Patient Summary: We have developed a tool that uses a person's characteristics to determine the risk of cancer if that person develops blood in the urine (haematuria). This can be used to help prioritise patients for further investigation., (Copyright © 2022 The Authors. Published by Elsevier B.V. All rights reserved.)

Objective: To evaluate the contemporary prevalence of urinary tract cancer (bladder cancer, upper tract urothelial cancer [UTUC] and renal cancer) in patients referred to secondary care with haematuria, adjusted for established patient risk markers and geographical variation., Patients and Methods: This was an international multicentre prospective observational study. We included patients aged ≥16 years, referred to secondary care with suspected urinary tract cancer. Patients with a known or previous urological malignancy were excluded. We estimated the prevalence of bladder cancer, UTUC, renal cancer and prostate cancer; stratified by age, type of haematuria, sex, and smoking. We used a multivariable mixed-effects logistic regression to adjust cancer prevalence for age, type of haematuria, sex, smoking, hospitals, and countries., Results: Of the 11 059 patients assessed for eligibility, 10 896 were included from 110 hospitals across 26 countries. The overall adjusted cancer prevalence (n = 2257) was 28.2% (95% confidence interval [CI] 22.3-34.1), bladder cancer (n = 1951) 24.7% (95% CI 19.1-30.2), UTUC (n = 128) 1.14% (95% CI 0.77-1.52), renal cancer (n = 107) 1.05% (95% CI 0.80-1.29), and prostate cancer (n = 124) 1.75% (95% CI 1.32-2.18). The odds ratios for patient risk markers in the model for all cancers were: age 1.04 (95% CI 1.03-1.05; P < 0.001), visible haematuria 3.47 (95% CI 2.90-4.15; P < 0.001), male sex 1.30 (95% CI 1.14-1.50; P < 0.001), and smoking 2.70 (95% CI 2.30-3.18; P < 0.001)., Conclusions: A better understanding of cancer prevalence across an international population is required to inform clinical guidelines. We are the first to report urinary tract cancer prevalence across an international population in patients referred to secondary care, adjusted for patient risk markers and geographical variation. Bladder cancer was the most prevalent disease. Visible haematuria was the strongest predictor for urinary tract cancer., (© 2021 The Authors BJU International published by John Wiley & Sons Ltd on behalf of BJU International.)

Introduction: Prostate incidentalomas are prostatic lesions suspicious for cancer discovered by imaging patients without a known history of prostatic cancer (PCa) for other reasons. 18-fluoro-2-deoxy-D-glucose positron emission tomography/computed tomography (FDG PET) is used to diagnose, stage, and assess response to treatment for numerous cancers, but it is not routinely used for PCa. We aimed to determine the rate of detection of prostate incidentalomas in patients undergoing FDG PET and to evaluate the natural history of these lesions., Methods: A retrospective review was conducted of all FDG PET scans performed between 2005 and 2017 at a single institution. Patients were selected who had prostatic uptake without a history of PCa. Clinical data were collected from electronic medical records to determine how the prostate incidentalomas were further evaluated and to define the rate of malignancy., Results: A prostate incidentaloma was identified in 309 (1.0%) of 31 019 FDG PET scans performed on men. A prostate-specific antigen (PSA) test was obtained in 40.1% of patients within six months of prostate incidentaloma detection. Six patients underwent a multiparametric magnetic resonance imaging (mpMRI) of the prostate, which identified PCa in one case. Overall, PCa was diagnosed in 33 cases, representing 10.7% of the prostate incidentalomas and 0.1% of the scanned patients. PCa was intermediate- or high-risk in 27 (8.7%) of the prostate incidentalomas., Conclusions: Incidental lesions detected in the prostate by FDG PET may represent clinically significant PCa. Referral to a urologist for further evaluation should be considered if the patient is otherwise in reasonable health.

Introduction: Radical cystectomy (RC) is a challenging procedure with significant morbidity, though remains the standard of care treatment for many patients with bladder cancer. There has been debate regarding the utility of universal risk calculators to aid in point-of-care prediction of complications in individual patients preoperatively. We retrospectively evaluated the predictive value of the ACS NSQIP universal surgical risk calculator in our patients who underwent RC., Methods: A prospective cohort of patients undergoing RC was retrospectively reviewed between October 2014 and August 2017. Only patients who underwent a RC for genitourinary cancer without significant deviation from NSQIP surgery codes 51590, 51595, and 51596 (n = 29) were included. The accuracy of the risk calculator was assessed by ROC AUC and Brier scores for both NSQIP and Clavien-Dindo defined complications. Additionally, each NSQIP risk factor was individually assessed for association with postoperative complications., Results: 223 patients who underwent open or robotic RC (n = 18) were included for analysis. Determined by AUC C-stat and Brier scores, prediction was good for cardiac complications (0.80 and 0.021), fair for pneumonia (0.75 and 0.017), poor for UTI (0.64 and 0.078), 30-day mortality (0.62 and 0.013), any complication (0.60 and 0.19) and serious complication (0.60 and 0.17). There was a significant discordance between the rate of NSQIP predicted vs. Clavien-Dindo observed any and serious complications: 28.8% vs. 67.3%, and 25.3% vs. 11.7%, respectively., Conclusion: The NSQIP universal surgical risk calculator did not perform with enough accuracy to consider adoption into clinical practice.

Purpose of Review: A bladder-preserving approach for high-grade nonmuscle invasive bladder cancer that has invaded the lamina propria (T1HG) may result in increased recurrence, progression, and even death from bladder cancer in some patients. Initial radical cystectomy does have increased cancer-specific survival (CSS), but represents significant overtreatment for many patients. An evidence-based, risk-stratified approach is required to select patients for immediate radical cystectomy in order to improve CSS., Recent Findings: A restaging transurethral resection aids in optimal staging and treatment of T1HG. Intravesical Bacillus Calmette-Guerin induction followed by 3 years of maintenance is the standard adjuvant management. However, when very high-risk (hydronephrosis, abnormal bimanual examination, variant histology, lymphovascular invasion, or residual disease on re-resection, and Bacillus Calmette-Guerin failure or early recurrence) or multiple high-risk factors (concomitant CIS, size >3 cm, multifocality, unfavorable tumor location, extensive lamina propria invasion, and elderly) are present, the risk of progression often outweighs the risk associated with radical cystectomy. In these cases, an immediate radical cystectomy likely provides an improved opportunity for cure compared to a bladder-preserving strategy., Summary: In order to increase the CSS of patients diagnosed with T1HG bladder cancer, an aggressive approach may benefit those with increased risk of progression.