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Background/Aim: Intermediate-risk prostate cancer (PCa) is a highly heterogeneous disease. Although low-dose-rate brachytherapy (LDR-BT) is mainly used for low- to intermediate-risk PCa, limited reports have evaluated the detailed differences in outcomes, including differences between patients with ISUP grade group (GG) 2 and GG3 intermediate-risk PCa. This study aimed to investigate the differences in outcomes between intermediate-risk Japanese patients with GG2 and GG3 PCa who underwent LDR-BT. Patients and Methods: This single-center retrospective study included 342 consecutive patients with intermediate-risk PCa; 232 patients with GG2 and 110 with GG3 were treated with LDR-BT at Tokushima University Hospital between July 2004 and December 2019. Results: No significant difference in 5-year biochemical progression-free survival and cancer-specific survival was observed between patients with GG2 and those with GG3 (p=0.649 and p=0.633, respectively). Multivariate analysis showed that radiation doses up to 90% of the prostate volume (D90) and the percentage of positive cores were predictors of recurrence in all patients with intermediate-risk PCa. Group analyses showed that D90 was a predictor for recurrence in patients with GG2. In contrast, a high percentage of positive cores was a significant risk factor for recurrence in patients with GG3. Conclusion: Positive core ratios observed on prostate biopsy correlated with higher recurrence rates after LDR-BT. This indicates that the proportion of positive cores in the biopsy may be an important factor in predicting the likelihood of recurrence, especially for patients with GG3 PCa.

Abstract Objectives The consensus conference of the International Society of Urological Pathology (ISUP), held in 2012, made recommendations regarding prognostic parameters of renal tumors. There was a strong consensus that tumor morphotype, pathologic tumor stage, and tumor grade are prognostic indicators of poor outcome. It was also agreed upon that prognostic significance of tumor necrosis is in evolution, and both microscopic and macroscopic tumor necrosis should be documented in percentages. The aim of our study was to explore the impact of tumor necrosis on metastasis-free survival in clear cell renal carcinomas (ccRCCs) in Pakistani patients. Methods We retrieved 318 consecutive in-house cases of ccRCC resections from 2014 to 2020 through hospital archives. Histologic slide review was done for assessment of tumor necrosis, tumor stage, and World Health Organization/ISUP grade. The follow-up data to assess metastasis-free survival were available in hospital archives. Results In multivariable analysis performed by logistic regression model, tumor necrosis was an independent poor prognostic indicator (P = .0001): group 1 (reference group), 0% necrosis; group 2, 1% to 10% necrosis (adjusted odds ratio [AOR], 8.71; 95% confidence interval [CI], 3.62-20.98); and group 3, more than 10% necrosis (AOR, 9.48; 95% CI, 3.99-22.725). Conclusions Tumor necrosis is an independent predictor of poor outcome in ccRCCs.

PurposeThe aim of this study is to investigate the role of [68Ga]Ga-PSMA-11 PET radiomics for the prediction of post-surgical International Society of Urological Pathology (PSISUP) grade in primary prostate cancer (PCa).MethodsThis retrospective study included 47 PCa patients who underwent [68Ga]Ga-PSMA-11 PET at IRCCS San Raffaele Scientific Institute before radical prostatectomy. The whole prostate was manually contoured on PET images and 103 image biomarker standardization initiative (IBSI)-compliant radiomic features (RFs) were extracted. Features were then selected using the minimum redundancy maximum relevance algorithm and a combination of the 4 most relevant RFs was used to train 12 radiomics machine learning models for the prediction of PSISUP grade: ISUP ≥ 4 vs ISUP < 4. Machine learning models were validated by means of fivefold repeated cross-validation, and two control models were generated to assess that our findings were not surrogates of spurious associations. Balanced accuracy (bACC) was collected for all generated models and compared with Kruskal–Wallis and Mann–Whitney tests. Sensitivity, specificity, and positive and negative predictive values were also reported to provide a complete overview of models’ performance. The predictions of the best performing model were compared against ISUP grade at biopsy.ResultsISUP grade at biopsy was upgraded in 9/47 patients after prostatectomy, resulting in a bACC = 85.9%, SN = 71.9%, SP = 100%, PPV = 100%, and NPV = 62.5%, while the best-performing radiomic model yielded a bACC = 87.6%, SN = 88.6%, SP = 86.7%, PPV = 94%, and NPV = 82.5%. All radiomic models trained with at least 2 RFs (GLSZM—Zone Entropy and Shape—Least Axis Length) outperformed the control models. Conversely, no significant differences were found for radiomic models trained with 2 or more RFs (Mann–Whitney p > 0.05).ConclusionThese findings support the role of [68Ga]Ga-PSMA-11 PET radiomics for the accurate and non-invasive prediction of PSISUP grade.

Prostate cancer pathology plays a crucial role in clinical management but is time-consuming. Artificial intelligence (AI) shows promise in detecting prostate cancer and grading patterns. We tested an AI-based digital twin of a pathologist, vPatho, on 2603 histological images of prostate tissue stained with hematoxylin and eosin. We analyzed various factors influencing tumor grade discordance between the vPatho system and six human pathologists. Our results demonstrated that vPatho achieved comparable performance in prostate cancer detection and tumor volume estimation, as reported in the literature. The concordance levels between vPatho and human pathologists were examined. Notably, moderate to substantial agreement was observed in identifying complementary histological features such as ductal, cribriform, nerve, blood vessel, and lymphocyte infiltration. However, concordance in tumor grading decreased when applied to prostatectomy specimens (κ = 0.44) compared to biopsy cores (κ = 0.70). Adjusting the decision threshold for the secondary Gleason pattern from 5 to 10% improved the concordance level between pathologists and vPatho for tumor grading on prostatectomy specimens (κ from 0.44 to 0.64). Potential causes of grade discordance included the vertical extent of tumors toward the prostate boundary and the proportions of slides with prostate cancer. Gleason pattern 4 was particularly associated with this population. Notably, the grade according to vPatho was not specific to any of the six pathologists involved in routine clinical grading. In conclusion, our study highlights the potential utility of AI in developing a digital twin for a pathologist. This approach can help uncover limitations in AI adoption and the practical application of the current grading system for prostate cancer pathology.

Purpose To assess if PSMA PET quantitative parameters are associated with pathologic ISUP grade group (GG) and upgrading/downgrading. Methods PCa patients undergoing radical prostatectomy with or without pelvic lymph node dissection staged with preoperative PSMA PET at seven referral centres worldwide were evaluated. PSMA PET parameters which included SUV max , PSMA volume , and total PSMA accumulation (PSMA total ) were collected. Multivariable logistic regression evaluated the association between PSMA PET quantified parameters and surgical ISUP GG. Decision-tree analysis was performed to identify discriminative thresholds for all three parameters related to the five ISUP GGs The ROC-derived AUC was used to determine whether the inclusion of PSMA quantified parameters improved the ability of multivariable models to predict ISUP GG ≥ 4. Results A total of 605 patients were included. Overall, 2%, 37%, 37%, 10% and 13% patients had pathologic ISUP GG1, 2, 3, 4, and 5, respectively. At multivariable analyses, all three parameters SUV max , PSMA volume and PSMA total were associated with GG ≥ 4 at surgical pathology after accounting for PSA and clinical T stage based on DRE, hospital and radioligand (all p  < 0.05). Addition of all three parameters significantly improved the discrimination of clinical models in predicting GG ≥ 4 from 68% (95%CI 63 – 74) to 74% (95%CI 69 – 79) for SUV max , 72% (95%CI 67 – 76) for PSMA volume , 74% (70 – 79) for PSMA total and 75% (95%CI 71 – 80) when all parameters were included (all p  < 0.05). Decision-tree analysis resulted in thresholds that discriminate between GG (SUV max 0–6.5, 6.5–15, 15–28, > 28, PSMA vol 0–2, 2–9, 9–20 and > 20 and PSMA total 0–12, 12–98 and > 98). PSMA volume was significantly associated with GG upgrading (OR 1.03 95%CI 1.01 – 1.05). In patients with biopsy GG1-3, PSMA volume  ≥ 2 was significantly associated with higher odds for upgrading to ISUP GG ≥ 4, compared to PSMA volume  < 2 (OR 6.36, 95%CI 1.47 – 27.6). Conclusion Quantitative PSMA PET parameters are associated with surgical ISUP GG and upgrading. We propose clinically relevant thresholds of these parameters which can improve in PCa risk stratification in daily clinical practice.

Exploring the value of predicting the WHO/ISUP grade of clear cell renal cell carcinoma (ccRCC) using computed tomography urography (CTU) images, providing valuable recommendations for the treatment of ccRCC. CTU images from the Renmin Hospital of Wuhan University (RHWU) cohort, including 328 patients with ccRCC, were retrospectively collected. The corticomedullary (CMP) phase features of ccRCC were extracted from the CTU images using the Pyradiomics package, and key features were selected through the Least Absolute Shrinkage and Selection Operator (LASSO) regression. The 328 patients were split into training and testing sets in a 7:3 ratio. 175 patients from the The Cancer Genome Atlas (TCGA) cohort were used for the external validation set. Various models, including Logistic Regression (LR), Multilayer Perceptron (MLP), Support Vector Machine (SVM), and eXtreme Gradient Boosting (XGBoost), were employed to predict the ISUP grade. SHAP analysis was then used to visualize the performance of the best model. A total of 1,218 features were extracted using the Pyradiomics package, with 20 features selected for model training through LASSO analysis. In the training set, the AUC for the LR model was 0.88 (95% confidence interval [CI] 0.84-0.91), for MLP it was 0.89 (95% CI 0.86-0.93), for SVM it was 0.86 (95% CI 0.83-0.90), and for XGBoost it was 0.96 (95% CI 0.92-0.99). In the testing set, the AUC for LR was 0.79 (95% CI 0.73-0.85), for MLP it was 0.78 (95% CI 0.72-0.83), for SVM it was 0.78 (95% CI 0.73-0.82), and for XGBoost it was 0.80 (95% CI 0.75-0.85). In the validation set, the AUC for LR was 0.74 (95% CI 0.68-0.79), for MLP it was 0.68 (95% CI 0.63-0.73), for SVM it was 0.67 (95% CI 0.64-0.71), and for XGBoost it was 0.78 (95% CI 0.74-0.83). XGBoost demonstrated superior performance, with a sensitivity of 0.99 (95% CI 0.96-1.00) in the training set, 0.92 (95% CI 0.88-0.97) in the testing set and 0.91 (95% CI 0.86,0.95) in validation set. SHAP analysis revealed that the wavelet-LHL_glcm_Idn and wavelet-LHL_glrlm_LongRunEmphasis features played pivotal roles in the classification task. In this study, we employ an artificial intelligence model to conduct non-invasive ISUP grade prediction on preoperative CTU images of ccRCC, thereby aiding clinical decision-making. Additionally, we uncover that the radiomics features extracted from the CMP phase of CTU images hold promise as potential biomarkers for grading ccRCC.

Background Small (< 4 cm) clear cell renal cell carcinoma (ccRCC) is the most common type of small renal cancer and its prognosis is poor. However, conventional radiological characteristics obtained by computed tomography (CT) are not sufficient to predict the nuclear grade of small ccRCC before surgery. Methods A total of 113 patients with histologically confirmed ccRCC were randomly assigned to the training set (n = 67) and the testing set (n = 46). The baseline and CT imaging data of the patients were evaluated statistically to develop a clinical model. A radiomics model was created, and the radiomics score (Rad-score) was calculated by extracting radiomics features from the CT images. Then, a clinical radiomics nomogram was developed using multivariate logistic regression analysis by combining the Rad-score and critical clinical characteristics. The receiver operating characteristic (ROC) curve was used to evaluate the discrimination of small ccRCC in both the training and testing sets. Results The radiomics model was constructed using six features obtained from the CT images. The shape and relative enhancement value of the nephrographic phase (REV of the NP) were found to be independent risk factors in the clinical model. The area under the curve (AUC) values for the training and testing sets for the clinical radiomics nomogram were 0.940 and 0.902, respectively. Decision curve analysis (DCA) revealed that the radiomics nomogram model was a better predictor, with the highest degree of coincidence. Conclusion The CT-based radiomics nomogram has the potential to be a noninvasive and preoperative method for predicting the WHO/ISUP grade of small ccRCC.

Histopathological evaluation is necessary for the diagnosis and grading of prostate cancer, which is still one of the most common cancers in men globally. Traditional evaluation is time-consuming, prone to inter-observer variability, and challenging to scale. The clinical usefulness of current AI systems is limited by the need for comprehensive pixel-level annotations. The objective of this research is to develop and evaluate a large-scale benchmarking study on a weakly supervised deep learning framework that minimizes the need for annotation and ensures interpretability for automated prostate cancer diagnosis and International Society of Urological Pathology (ISUP) grading using whole slide images (WSIs). This study rigorously tested six cutting-edge multiple instance learning (MIL) architectures (CLAM-MB, CLAM-SB, ILRA-MIL, AC-MIL, AMD-MIL, WiKG-MIL), three feature encoders (ResNet50, CTransPath, UNI2), and four patch extraction techniques (varying sizes and overlap) using the PANDA dataset (10,616 WSIs), yielding 72 experimental configurations. The methodology used distributed cloud computing to process over 31 million tissue patches, implementing advanced attention mechanisms to ensure clinical interpretability through Grad-CAM visualizations. The optimum configuration (UNI2 encoder with ILRA-MIL, 256 256 patches, 50% overlap) achieved 78.75% accuracy and 90.12% quadratic weighted kappa (QWK), outperforming traditional methods and approaching expert pathologist-level diagnostic capability. Overlapping smaller patches offered the best balance of spatial resolution and contextual information, while domain-specific foundation models performed noticeably better than generic encoders. This work is the first large-scale, comprehensive comparison of weekly supervised MIL methods for prostate cancer diagnosis and grading. The proposed approach has excellent clinical diagnostic performance, scalability, practical feasibility through cloud computing, and interpretability using visualization tools.

ObjectivesThis study aimed to assess the predictive value of radiomics derived from intratumoral and peritumoral regions and to develop a radiomics nomogram to predict preoperative nuclear grade and overall survival (OS) in patients with clear cell renal cell carcinoma (ccRCC).MethodsThe study included 395 patients with ccRCC from our institution. The patients in Center A (anonymous) institution were randomly divided into a training cohort (n = 284) and an internal validation cohort (n = 71). An external validation cohort comprising 40 patients from Center B also was included. Computed tomography (CT) radiomics features were extracted from the internal area of the tumor (IAT) and IAT combined peritumoral areas of the tumor at 3 mm (PAT 3 mm) and 5 mm (PAT 5 mm). Independent predictors from both clinical and radiomics scores (Radscore) were used to construct a radiomics nomogram. Kaplan–Meier analysis with a log-rank test was performed to evaluate the correlation between factors and OS.ResultsThe PAT 5-mm radiomics model (RM) exhibited exceptional predictive capability for grading, achieving an area under the curves of 0.80, 0.80, and 0.90 in the training, internal validation, and external validation cohorts. The nomogram and RM gained from the PAT 5-mm region were more clinically useful than the clinical model. The association between OS and predicted nuclear grade derived from the PAT 5-mm Radscore and the nomogram-predicted score was statistically significant (p < 0.05).ConclusionThe CT-based radiomics and nomograms showed valuable predictive capabilities for the World Health Organization/International Society of Urological Pathology grade and OS in patients with ccRCC.Critical relevance statementThe intratumoral and peritumoral radiomics are feasible and promising to predict nuclear grade and overall survival in patients with clear cell renal cell carcinoma, which can contribute to the development of personalized preoperative treatment strategies.Key PointsThe multi-regional radiomics features are associated with clear cell renal cell carcinoma (ccRCC) grading and prognosis.The combination of intratumoral and peritumoral 5 mm regional features demonstrated superior predictive performance for grading.The nomogram and radiomics models have a broad range of clinical applications.

Background/Objectives: Magnetic resonance imaging (MRI) and MRI–ultrasound (US) fusion-targeted biopsy have improved prostate cancer diagnosis, particularly for clinically significant disease. However, the added value of combining systematic biopsy with targeted biopsy remains debated. This study aimed to evaluate the diagnostic accuracy of MRI–US fusion-targeted and systematic transperineal biopsies in detecting prostate cancer and explore the correlation between PI-RADS score and histology. Methods: We retrospectively analyzed 356 patients with 452 MRI-detected lesions who underwent both MRI–US fusion-targeted and transperineal systematic biopsies between 2020 and 2023. Clinically significant prostate cancer (csPCa) was defined as International Society of Urological Pathology (ISUP) grade ≥ 2. Diagnostic performance metrics (sensitivity, specificity, and accuracy) were calculated for each technique using the combined result as a reference. Subgroup analysis was performed for patients under active surveillance. Results: Prostate cancer was diagnosed in 323 of 452 lesions (71%) and csPCa in 223 lesions (49%). Targeted biopsy demonstrated higher sensitivity (93.7%) and accuracy (79.9%) than systematic biopsy (85.7% sensitivity and 77.6% accuracy), although systematic biopsy provided slightly higher specificity. Systematic biopsy alone identified 8.2% of PCa cases missed by targeted biopsy and upgraded 9.9% of lesions to csPCa. csPCa detection increased with PI-RADS score (23% in PI-RADS 3 and 73% in PI-RADS 5). In active surveillance patients, csPCa was found in 65% of lesions. Conclusions: MRI–US fusion-targeted biopsy improves csPCa detection, but systematic biopsy remains valuable, especially for identifying additional or higher-grade disease. The combined approach provides an optimal diagnostic yield, supporting its continued use in both initial and repeat biopsy settings.