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Background: Cytoreductive nephrectomy (CN) plays an important role in the treatment of a subgroup of metastatic renal cell carcinoma (mRCC) patients. Objective: We aimed to evaluate morbidity associated with this procedure and identify potential predictors thereof to aid patient selection for this procedure and potentially improve patient outcomes. Design, setting, and participants: Data from 736 mRCC patients undergoing CN at 14 institutions were retrospectively recorded in the Registry for Metastatic RCC (REMARCC). Outcome measurements and statistical analysis: Logistic regression analysis was used to identify predictors for intraoperative, any-grade (AGCs), low-grade, and high-grade (HGCs) postoperative complications (according to the Clavien-Dindo classification) as well as 30-d readmission rates. Results and limitations: Intraoperative complications were observed in 69 patients (10.9%

Background: Selection of patients for upfront cytoreductive nephrectomy (CN) in metastatic renal cell carcinoma (mRCC) has to be improved. Objective: To evaluate a new scoring system for the prediction of overall mortality (OM) in mRCC patients undergoing CN. Design, setting, and participants: We identified a total of 519 patients with synchronous mRCC undergoing CN between 2005 and 2019 from a multi-institutional registry (Registry for Metastatic RCC [REMARCC]). Outcome measurements and statistical analysis: Cox proportional hazard regression was used to test the main predictors of OM. Restricted mean survival time was estimated as a measure of the average overall survival time up to 36 mo of followup. The concordance index (C-index) was used to determine the model's discrimination. Decision curve analyses were used to compare the net benefit from the REMARCC model with International mRCC Database Consortium (IMDC) or Memorial Sloan Kettering Cancer Center (MSKCC) risk scores.

Purpose To evaluate the performance of combined PET and multiparametric MRI (mpMRI) radiomics for the group-wise prediction of postsurgical Gleason scores (psGSs) in primary prostate cancer (PCa) patients. Methods Patients with PCa, who underwent [68 Ga]Ga-PSMA-11 PET/MRI followed by radical prostatectomy, were included in this retrospective analysis (n = 101). Patients were grouped by psGS in three categories: ISUP grades 1–3, ISUP grade 4, and ISUP grade 5. mpMRI images included T1-weighted, T2-weighted, and apparent diffusion coefficient (ADC) map. Whole-prostate segmentations were performed on each modality, and image biomarker standardization initiative (IBSI)-compliant radiomic features were extracted. Nine support vector machine (SVM) models were trained: four single-modality radiomic models (PET, T1w, T2w, ADC); three PET + MRI double-modality models (PET + T1w, PET + T2w, PET + ADC), and two baseline models (one with patient data, one image-based) for comparison. A sixfold stratified cross-validation was performed, and balanced accuracies (bAcc) of the predictions of the best-performing models were reported and compared through Student’s t-tests. The predictions of the best-performing model were compared against biopsy GS (bGS). Results All radiomic models outperformed the baseline models. The best-performing (mean ± stdv [%]) single-modality model was the ADC model (76 ± 6%), although not significantly better (p > 0.05) than other single-modality models (T1w: 72 ± 3%, T2w: 73 ± 2%; PET: 75 ± 5%). The overall best-performing model combined PET + ADC radiomics (82 ± 5%). It significantly outperformed most other double-modality (PET + T1w: 74 ± 5%, p = 0.026; PET + T2w: 71 ± 4%, p = 0.003) and single-modality models (PET: p = 0.042; T1w: p = 0.002; T2w: p = 0.003), except the ADC-only model (p = 0.138). In this initial cohort, the PET + ADC model outperformed bGS overall (82.5% vs 72.4%) in the prediction of psGS. Conclusion All single- and double-modality models outperformed the baseline models, showing their potential in the prediction of GS, even with an unbalanced cohort. The best-performing model included PET + ADC radiomics, suggesting a complementary value of PSMA-PET and ADC radiomics.

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