Muscle-invasive bladder cancer (MIBC) is an aggressive cancer with a high recurrence risk due to micrometastases. Standard treatment, neoadjuvant cisplatin-based chemotherapy followed by radical cystectomy, is not suitable for all patients, with many being ineligible or experiencing recurrence, alongside significant toxicity concerns.Current Concepts: The introduction of immune checkpoint inhibitors (ICIs) into the perioperative setting —including neoadjuvant ICI use in cisplatin-ineligible patients, adjuvant ICI use in high-risk individuals, and chemoimmunotherapy in either the preoperative or postoperative period—has demonstrated promising clinical outcomes. Additionally, bladder preservation strategies are currently under investigation in select patients who exhibit favorable treatment responses, aiming to maintain quality of life without compromising oncologic outcomes. Nevertheless, challenges such as overtreatment, long-term toxicity, and immune-related adverse events remain significant, underscoring the necessity for precise patient selection.Discussion and Conclusion: To personalize perioperative management of MIBC, it is essential to develop and clinically implement robust predictive biomarkers. Assessment of molecular residual disease using circulating tumor DNA is emerging as a promising method to stratify risk, guide adjuvant treatment decisions, and monitor therapeutic response in real time. Future research should prioritize the validation of these biomarkers, refinement of patient selection criteria for bladder preservation strategies, and evaluation of novel therapeutic agents such as antibody-drug conjugates and fibroblast growth factor receptor inhibitors in the perioperative setting. Ultimately, adopting a precision oncology approach will be critical for balancing oncologic efficacy with toxicity management and achieving patient-centered outcomes.

Muscle-invasive bladder cancer (MIBC) is an aggressive cancer with a high recurrence risk due to micrometastases. Standard treatment, neoadjuvant cisplatin-based chemotherapy followed by radical cystectomy, is not suitable for all patients, with many being ineligible or experiencing recurrence, alongside significant toxicity concerns.Current Concepts: The introduction of immune checkpoint inhibitors (ICIs) into the perioperative setting —including neoadjuvant ICI use in cisplatin-ineligible patients, adjuvant ICI use in high-risk individuals, and chemoimmunotherapy in either the preoperative or postoperative period—has demonstrated promising clinical outcomes. Additionally, bladder preservation strategies are currently under investigation in select patients who exhibit favorable treatment responses, aiming to maintain quality of life without compromising oncologic outcomes. Nevertheless, challenges such as overtreatment, long-term toxicity, and immune-related adverse events remain significant, underscoring the necessity for precise patient selection.Discussion and Conclusion: To personalize perioperative management of MIBC, it is essential to develop and clinically implement robust predictive biomarkers. Assessment of molecular residual disease using circulating tumor DNA is emerging as a promising method to stratify risk, guide adjuvant treatment decisions, and monitor therapeutic response in real time. Future research should prioritize the validation of these biomarkers, refinement of patient selection criteria for bladder preservation strategies, and evaluation of novel therapeutic agents such as antibody-drug conjugates and fibroblast growth factor receptor inhibitors in the perioperative setting. Ultimately, adopting a precision oncology approach will be critical for balancing oncologic efficacy with toxicity management and achieving patient-centered outcomes.

Muscle-invasive bladder cancer (MIBC) is an aggressive cancer with a high recurrence risk due to micrometastases. Standard treatment, neoadjuvant cisplatin-based chemotherapy followed by radical cystectomy, is not suitable for all patients, with many being ineligible or experiencing recurrence, alongside significant toxicity concerns.Current Concepts: The introduction of immune checkpoint inhibitors (ICIs) into the perioperative setting —including neoadjuvant ICI use in cisplatin-ineligible patients, adjuvant ICI use in high-risk individuals, and chemoimmunotherapy in either the preoperative or postoperative period—has demonstrated promising clinical outcomes. Additionally, bladder preservation strategies are currently under investigation in select patients who exhibit favorable treatment responses, aiming to maintain quality of life without compromising oncologic outcomes. Nevertheless, challenges such as overtreatment, long-term toxicity, and immune-related adverse events remain significant, underscoring the necessity for precise patient selection.Discussion and Conclusion: To personalize perioperative management of MIBC, it is essential to develop and clinically implement robust predictive biomarkers. Assessment of molecular residual disease using circulating tumor DNA is emerging as a promising method to stratify risk, guide adjuvant treatment decisions, and monitor therapeutic response in real time. Future research should prioritize the validation of these biomarkers, refinement of patient selection criteria for bladder preservation strategies, and evaluation of novel therapeutic agents such as antibody-drug conjugates and fibroblast growth factor receptor inhibitors in the perioperative setting. Ultimately, adopting a precision oncology approach will be critical for balancing oncologic efficacy with toxicity management and achieving patient-centered outcomes.

Purpose: Unnecessary prostate biopsies for detecting prostate cancer (PCa) should be minimized. Therefore, this study developed a machine learning (ML) model to predict PCa in Korean men and evaluated its usability. Materials and Methods: We retrospectively analyzed clinical data from 928 patients who underwent prostate biopsies at Kangwon National University Hospital between May 2013 and May 2023. Of these, 377 (41.6%) were diagnosed with PCa, and 551 (59.4%) did not have cancer. For external validation, clinical data from 385 patients aged 48–89 years who underwent prostate biopsies from September 2005 to September 2023 at Wonju Severance Christian Hospital were also included. Twenty-two clinical features were used to develop an ML model to predict PCa. Features were selected based on their contributions to model performance, leading to the inclusion of 15 features. A meta-learner was constructed using logistic regression to predict the probability of PCa, and the classifier was trained and validated on randomly extracted training and test sets at an 8:2 ratio. Results: The prostate health index, prostate volume, age, nodule on digital rectal examination, and prostate-specific antigen were the top 5 features for predicting PCa. The area under the receiver operating characteristic curve (AUC) of the meta-learner logistic regression model was 0.89, and the accuracy, sensitivity, and specificity were 0.828, 0.711, and 0.909, respectively. Our model also showed excellent prediction performance for high-grade PCa, with a Gleason score of 7 or higher and an AUC of 0.903. Furthermore, we evaluated the performance of the model using external cohort clinical data and achieved an AUC of 0.863. Conclusions Our ML model excelled in predicting PCa, specifically clinically significant PCa. Although extensive cross-validation in other clinical cohorts is needed, this ML model is a promising option for future diagnostics.

Purpose: Unnecessary prostate biopsies for detecting prostate cancer (PCa) should be minimized. Therefore, this study developed a machine learning (ML) model to predict PCa in Korean men and evaluated its usability. Materials and Methods: We retrospectively analyzed clinical data from 928 patients who underwent prostate biopsies at Kangwon National University Hospital between May 2013 and May 2023. Of these, 377 (41.6%) were diagnosed with PCa, and 551 (59.4%) did not have cancer. For external validation, clinical data from 385 patients aged 48–89 years who underwent prostate biopsies from September 2005 to September 2023 at Wonju Severance Christian Hospital were also included. Twenty-two clinical features were used to develop an ML model to predict PCa. Features were selected based on their contributions to model performance, leading to the inclusion of 15 features. A meta-learner was constructed using logistic regression to predict the probability of PCa, and the classifier was trained and validated on randomly extracted training and test sets at an 8:2 ratio. Results: The prostate health index, prostate volume, age, nodule on digital rectal examination, and prostate-specific antigen were the top 5 features for predicting PCa. The area under the receiver operating characteristic curve (AUC) of the meta-learner logistic regression model was 0.89, and the accuracy, sensitivity, and specificity were 0.828, 0.711, and 0.909, respectively. Our model also showed excellent prediction performance for high-grade PCa, with a Gleason score of 7 or higher and an AUC of 0.903. Furthermore, we evaluated the performance of the model using external cohort clinical data and achieved an AUC of 0.863. Conclusions Our ML model excelled in predicting PCa, specifically clinically significant PCa. Although extensive cross-validation in other clinical cohorts is needed, this ML model is a promising option for future diagnostics.

Purpose: Unnecessary prostate biopsies for detecting prostate cancer (PCa) should be minimized. Therefore, this study developed a machine learning (ML) model to predict PCa in Korean men and evaluated its usability. Materials and Methods: We retrospectively analyzed clinical data from 928 patients who underwent prostate biopsies at Kangwon National University Hospital between May 2013 and May 2023. Of these, 377 (41.6%) were diagnosed with PCa, and 551 (59.4%) did not have cancer. For external validation, clinical data from 385 patients aged 48–89 years who underwent prostate biopsies from September 2005 to September 2023 at Wonju Severance Christian Hospital were also included. Twenty-two clinical features were used to develop an ML model to predict PCa. Features were selected based on their contributions to model performance, leading to the inclusion of 15 features. A meta-learner was constructed using logistic regression to predict the probability of PCa, and the classifier was trained and validated on randomly extracted training and test sets at an 8:2 ratio. Results: The prostate health index, prostate volume, age, nodule on digital rectal examination, and prostate-specific antigen were the top 5 features for predicting PCa. The area under the receiver operating characteristic curve (AUC) of the meta-learner logistic regression model was 0.89, and the accuracy, sensitivity, and specificity were 0.828, 0.711, and 0.909, respectively. Our model also showed excellent prediction performance for high-grade PCa, with a Gleason score of 7 or higher and an AUC of 0.903. Furthermore, we evaluated the performance of the model using external cohort clinical data and achieved an AUC of 0.863. Conclusions Our ML model excelled in predicting PCa, specifically clinically significant PCa. Although extensive cross-validation in other clinical cohorts is needed, this ML model is a promising option for future diagnostics.

Purpose: To create a nomogram that can predict the probability of prostate cancer using prostate health index (PHI) and clinical parameters of patients. And the optimal cut-off value of PHI for prostate cancer was also assessed. Materials and Methods: A prospective, multi-center study was conducted. PHI was evaluated prior to biopsy in patients requiring prostate biopsy due to high prostate-specific antigen (PSA). Among screened 1,010 patients, 626 patients with clinically suspected prostate cancer with aged 40 to 85 years, and with PSA levels ranging from 2.5 to 10 ng/mL were analyzed. Results: Among 626 patients, 38.82% (243/626) and 22.52% (141/626) were diagnosed with prostate cancer and clinically significant prostate cancer, respectively. In the PSA 2.5 to 4 ng/mL group, the areas under the curve (AUCs) of the nomograms for overall prostate cancer and clinically significant prostate cancer were 0.796 (0.727–0.866; p<0.001), and 0.697 (0.598–0.795; p=0.001), respectively. In the PSA 4 to 10 ng/mL group, the AUCs of nomograms for overall prostate cancer and clinically significant prostate cancer were 0.812 (0.783–0.842; p<0.001), and 0.839 (0.810–0.869; p<0.001), respectively. Conclusions Even though external validations are necessary, a nomogram using PHI might improve the prediction of prostate cancer, reducing the need for prostate biopsies.

Jaundice is a rare symptom of the paraneoplastic syndrome associated with prostate cancer. We report a case of metastatic prostate cancer that presented as jaundice. There was an absence of biliary obstruction and hepatic metastasis; therefore, the paraneoplastic syndrome was suggested as the etiology of cholestasis. Jaundice improved with the treatment of prostate cancer. In the literature, interleukin-6 has been suggested to be associated with paraneoplastic syndrome.

Jaundice is a rare symptom of the paraneoplastic syndrome associated with prostate cancer. We report a case of metastatic prostate cancer that presented as jaundice. There was an absence of biliary obstruction and hepatic metastasis; therefore, the paraneoplastic syndrome was suggested as the etiology of cholestasis. Jaundice improved with the treatment of prostate cancer. In the literature, interleukin-6 has been suggested to be associated with paraneoplastic syndrome.

Purpose: We evaluated the clinical performance of Prostate Health Index (PHI) density with [-2]proPSA (p2PSA)and its derivatives in predicting the presence of prostate cancer (PCa) in Korean men. Materials and Methods: A total of 706 men with total prostate-specific antigen (tPSA)≥2.5 ng/mL who underwenttheir first prostate biopsy were included in this prospective, multicenter, observational study. Diagnostic accuracyof tPSA, free-to-total PSA ratio (%fPSA), p2PSA, %p2PSA, the Beckman Coulter PHI, and PHI density wasassessed by receiver operating characteristic curve analyses and logistic regression analyses. PHI was calculatedas [(p2PSA/free PSA)×tPSA½], and density calculations were performed using prostate volume as determinedby transrectal ultrasonography. Results: Overall, PCa was detected in 367 of all subjects (52%). In men with tPSA 2.5–10 ng/mL, the detectionrate of PCa was 41.1% (188 of 457). In this group, PHI and PHI density were the most accurate predictorsof PCa and significantly outperformed tPSA and %fPSA; area under the curve for tPSA, %fPSA, %p2PSA, PHI,and PHI density was 0.58, 0.68, 0.70, 0.75, 0.73 respectively. PHI and PHI density were also the strongestpredictor of PCa with Gleason score ≥7. Conclusions Based on the present prospective multicenter experience, PHI and PHI density demonstrate thesuperior clinical performance in predicting the presence of PCa in Korean men with tPSA 2.5–10 ng/mL.