Introduction Testicular cancer (TC), though highly curable, can carry significant morbidity and mortality when diagnosis is delayed. Herein, we characterize the distribution and prognostic impact of diagnostic delay on stage at presentation, disease progression, and mortality in a Mexican TC cohort. Patients We retrospectively analyzed 223 men with histologically confirmed TC treated at a single tertiary referral center in Mexico City between January 2017 and January 2025. The analyzable cohort was defined after excluding patients with incomplete clinical data or fewer than one month of post-orchiectomy follow-up. Methods The primary analysis evaluated associations between diagnostic delay and clinical outcomes, using penalized regression for mortality because of complete separation. Secondarily, five machine-learning models were cross-validated to assess whether routine clinical variables improved prediction beyond established prognostic factors. Results Median age was 26 years; 53.4% had non-seminomatous histology and 25.6% had diagnostic delay >6 months. All 13 deaths occurred in stage IIIC disease, while 56/57 delayed patients progressed. Mortality was associated with delay >6 months (OR 10.3), poor-risk IGCCCG disease (OR 17.2), and AFP >10,000 ng/mL (OR 5.3). Cross-validated AUCs ranged from 0.830 to 0.863, with no consistently superior model. Conclusion Diagnostic delay >6 months was strongly associated with advanced disease, progression, and mortality, although this association was largely mediated by stage at presentation. Machine learning models did not identify prognostic information beyond established clinical factors. Microabstract We retrospectively analyzed 223 men with confirmed testicular cancer to assess the prognostic impact of diagnostic delay. Delays longer than 6 months occurred almost exclusively in stage III disease and were strongly associated with progression and mortality (penalized odds ratio 10.3), although largely explained by advanced stage at presentation.
Introduction The Comprehensive Complication Index (CCI) is a novel system based on the Clavien-Dindo Classification (CDC), offering a more comprehensive scoring framework for postoperative adverse events by accumulating all occurred complications. This study aimed to evaluate the ability of the CCI to demonstrate the complication burden and predict length of stay (LOS) in patients undergoing robot-assisted laparoscopic prostatectomy (RALP). Materials and Methods Patients who underwent RALP between 2019 and 2025 at a single tertiary-referral academic center were included in the study. Complications were recorded according to the CDC. Descriptive statistics and linear/logistic regression analyses were conducted to reveal the relationship between clinical predictors and RALP outcomes in detail. Results A total of 421 patients were included, of whom 118 (28%) developed at least one complication. The complication group had higher median blood loss and longer LOS (170 [145–210] vs. 235 [181–300] mL and 3 [2–3] vs. 3 [2–3] days, both p<0.001). The correlation of the cumulative CCI with LOS was marginally stronger than that of the highest CDC grade (CCI r=0.55; CDC r=0.53; both p<0.001). As the two indices were nearly collinear (r≈0.99), this difference is small and should be interpreted cautiously; in separate models the adjusted R² for LOS was slightly higher for the CCI (0.30 vs. 0.27). In multivariable analysis, blood loss (β=0.012, p<0.001) and major complications (β=1.66, p<0.001) were independent predictors of prolonged LOS. Conclusion The CCI is a reliable index for demonstrating complication burden and is more accurate than the CDC in predicting LOS.
Introduction Bayesian statistics provide direct probabilistic interpretations that align with clinical decision-making in prostate cancer. Unlike frequentist approaches, which yield p-values and confidence intervals, Bayesian methods generate posterior probabilities that directly address patient-facing questions. This review synthesizes Bayesian applications across the prostate cancer continuum. Methods We conducted a narrative review informed by a structured search of PubMed, Web of Science, and the Cochrane Library. Searches yielded 465 records; after removal of 144 duplicates, 321 were screened and 11 representative studies were selected. The review was prepared with reference to the Scale for the Assessment of Narrative Review Articles (SANRA) guidance. Results Bayesian methods produced clinically interpretable probabilities and individualized predictions across prostate cancer care. Joint modeling of longitudinal prostate-specific antigen with time-to-event outcomes enabled dynamic recurrence forecasting, and risk-triggered active surveillance schedules reduced biopsies while maintaining timely upgrading detection. Bayesian network meta-analyses ranked systemic therapies and imaging tracers. Adaptive, model-based designs optimized radiotherapy and radioligand dosing, while population pharmacokinetic/pharmacodynamic frameworks supported hypothesis-generating personalization. Bayesian networks also improved causal adjustment in observational studies. Collectively, these applications enhanced evidence synthesis, decision transparency, patient communication, and model calibration. Conclusion Bayesian statistics complement frequentist methods by providing interpretable probabilities, incorporating prior knowledge, and supporting transparent clinical decisions. Familiarity with key concepts—posterior probabilities, credible intervals, prior specification, and calibration—enables clinicians to critically appraise Bayesian studies and communicate results effectively. As prostate cancer management increasingly emphasizes longitudinal monitoring and personalized treatment, Bayesian methods offer natural frameworks for evidence synthesis and decision-making under uncertainty. Microabstract Bayesian statistics offer clinically interpretable probabilistic frameworks for prostate cancer research. This review demonstrates applications across active surveillance, treatment sequencing, imaging synthesis, and adaptive trials, providing urologists with practical guidance for interpreting Bayesian studies and their direct patient-facing probability statements that complement traditional frequentist approaches.
