Machine-learning Algorithm-based Risk Prediction and Screening-detected Prostate Cancer in A Benign Prostate Hyperplasia Cohort.

BACKGROUND/AIM: Prostate cancer (PCa) is lethal. Our aim in this retrospective cohort study was to use machine learning-based methodology to predict PCa risk in patients with benign prostate hyperplasia (BPH), identify potential risk factors, and optimize predictive performance. PATIENTS AND METHODS: The dataset was extracted from a clinical information database of patients at a single institute from January 2000 to December 2020. Patients newly diagnosed with BPH and prescribed alpha blockers/5-alpha-reductase inhibitors were enrolled. Patients were excluded if they had a previous diagnosis of any cancer or were diagnosed with PCa within 1 month of enrolment. The study endpoint was PCa diagnosis. The study utilized the extreme gradient boosting (XGB), support vector machine (SVM) and K-nearest neighbors (KNN) machine-learning algorithms for analysis. RESULTS: The dataset used in this study included 5,122 medical records of patients with and without PCa, with 19 patient characteristics. The SVM and XGB models performed better than the KNN model in terms of accuracy and area under curve. Local interpretable model-agnostic explanation and Shapley additive explanations analysis showed that body mass index (BMI) and late prostate-specific antigen (PSA) were important features for the SVM model, while PSA velocity, late PSA, and BMI were important features for the XGB model. Use of 5-alpha-reductase inhibitor was associated with a higher incidence of PCa, with similar survival outcomes compared to non-users. CONCLUSION: Machine learning can enhance personalized PCa risk assessments for patients with BPH but more research is necessary to refine these models and address data biases. Clinicians should use them as supplementary tools alongside traditional screening methods.

Metastasectomy Improves Overall Survival in Metastatic Renal Cell Carcinoma: A Retrospective Cohort Study.

BACKGROUND/AIM: The use of complete metastasectomy for treating metastatic renal cell carcinoma (mRCC) has been shown to improve survival outcomes in the era of tyrosine kinase inhibitors (TKIs). However, its effectiveness in combination with immune checkpoint inhibitors (ICIs) remains unclear. Therefore, the objective of the study was to elucidate the impact of metastasectomy in patients with mRCC who received both TKIs or ICIs. PATIENTS AND METHODS: A total of 157 patients diagnosed with metastatic renal cell carcinoma (mRCC) between 2006 and 2018 in Taichung Veterans General Hospital were included in the study. Patients were divided into two groups: the non-metastasectomy group (n=89) and the metastasectomy group (n=68). Kaplan-Meier analyses and Cox proportional hazards models were employed to evaluate the impact of metastasectomy and other risk factors on overall survival (OS). RESULTS: Among patients who underwent metastasectomy, 62 patients (91.18%) underwent metastasectomy for more than 50% of their metastatic sites, and 42 patients (61.76%) received complete metastasectomy. The median overall survival was 55.75 months in the metastasectomy group, which was significantly longer than the 15.14 months observed in the non-metastasectomy group (p<0.001). Multivariate regression analysis revealed that metastasectomy had a significant impact on overall survival [hazard ratio (HR)=0.42, 95% confidence interval (CI)=0.26-0.67, p<0.001]. Additionally, performance status and lactate dehydrogenase were identified as independent predictors for overall survival. CONCLUSION: Combination of metastasectomy with systemic therapy was shown to improve overall survival in patients with mRCC. Therefore, this modality may be considered as a viable option for patients who are fit for surgical intervention and are undergoing treatment with either TKIs or ICIs.