Purpose: Active surveillance (AS) is one of the management options for patients with low-risk and select intermediate-risk prostate cancer (PC). However, factors predicting disease reclassification and conversion to active treatment from a large population of pure Asian cohorts regarding AS are less evaluated. This study investigated the intermediate-term outcomes of patients with localized PC undergoing AS. Materials and Methods: This cohort study enrolled consecutive men with localized non-high-risk PC diagnosed in Taiwan between June 2012 and Jan 2023. The study endpoints were disease reclassification (either pathological or radiographic progression) and conversion to active treatment. The factors predicting endpoints were evaluated using the Cox proportional hazards model. Results: A total of 405 patients (median age: 67.2 years) were consecutively enrolled and followed up with a median of 64.6 months. Based on the National Comprehensive Cancer Network (NCCN) risk grouping, 70 (17.3%), 164 (40.5%), 140 (34.6%), and 31 (7.7%) patients were classified as very low-risk, low-risk, favorable-intermediate risk, and unfavorable intermediate-risk PC, respectively. The 5-year reclassification rates were 24.8%, 27.0%, 18.6%, and 25.3%, respectively. The 5-year conversion rates were 20.4%, 28.8%, 43.6%, and 37.8%, respectively. A prostate-specific antigen density (PSAD) of ≥0.15 ng/mL2 predicted reclassification (hazard ratio [HR] 1.84, 95% confidence interval [CI] 1.17–2.88) and conversion (HR 1.56, 95% CI 1.05–2.31). A maximal percentage of cancer in positive cores (MPCPC) of ≥15% predicted conversion (15% to <50%: HR 1.41, 95% CI 0.91–2.18; ≥50%: HR 1.97, 95% CI 1.1453–3.40) compared with that of <15%. A Gleason grade group (GGG) of 3 tumor also predicted conversion (HR 2.69, 95% CI 1.06–6.79; GGG 3 vs 1). One patient developed metastasis, but none died of PC during the study period (2,141 person-years). Conclusions AS is a viable option for Taiwanese men with non-high-risk PC, in terms of reclassification and conversion. High PSAD predicted reclassification, whereas high PSAD, MPCPC, and GGG predicted conversion.

Background/Aims: Chronic hepatitis C (CHC) patients who failed antiviral therapy are at increased risk for hepatocellular carcinoma (HCC). This study assessed the potential role of metformin and statins, medications for diabetes mellitus (DM) and hyperlipidemia (HLP), in reducing HCC risk among these patients. Methods: We included CHC patients from the T-COACH study who failed antiviral therapy. We tracked the onset of HCC 1.5 years post-therapy by linking to Taiwan’s cancer registry data from 2003 to 2019. We accounted for death and liver transplantation as competing risks and employed Gray’s cumulative incidence and Cox subdistribution hazards models to analyze HCC development. Results: Out of 2,779 patients, 480 (17.3%) developed HCC post-therapy. DM patients not using metformin had a 51% increased risk of HCC compared to non-DM patients, while HLP patients on statins had a 50% reduced risk compared to those without HLP. The 5-year HCC incidence was significantly higher for metformin non-users (16.5%) versus non-DM patients (11.3%; adjusted sub-distribution hazard ratio [aSHR]=1.51; P=0.007) and metformin users (3.1%; aSHR=1.59; P=0.022). Statin use in HLP patients correlated with a lower HCC risk (3.8%) compared to non-HLP patients (12.5%; aSHR=0.50; P<0.001). Notably, the increased HCC risk associated with non-use of metformin was primarily seen in non-cirrhotic patients, whereas statins decreased HCC risk in both cirrhotic and non-cirrhotic patients. Conclusions Metformin and statins may have a chemopreventive effect against HCC in CHC patients who failed antiviral therapy. These results support the need for personalized preventive strategies in managing HCC risk.

Background/Aims: Despite the high efficacy of direct-acting antivirals (DAAs), approximately 1–3% of hepatitis C virus (HCV) patients fail to achieve a sustained virological response. We conducted a nationwide study to investigate risk factors associated with DAA treatment failure. Machine-learning algorithms have been applied to discriminate subjects who may fail to respond to DAA therapy. Methods: We analyzed the Taiwan HCV Registry Program database to explore predictors of DAA failure in HCV patients. Fifty-five host and virological features were assessed using multivariate logistic regression, decision tree, random forest, eXtreme Gradient Boosting (XGBoost), and artificial neural network. The primary outcome was undetectable HCV RNA at 12 weeks after the end of treatment. Results: The training (n=23,955) and validation (n=10,346) datasets had similar baseline demographics, with an overall DAA failure rate of 1.6% (n=538). Multivariate logistic regression analysis revealed that liver cirrhosis, hepatocellular carcinoma, poor DAA adherence, and higher hemoglobin A1c were significantly associated with virological failure. XGBoost outperformed the other algorithms and logistic regression models, with an area under the receiver operating characteristic curve of 1.000 in the training dataset and 0.803 in the validation dataset. The top five predictors of treatment failure were HCV RNA, body mass index, α-fetoprotein, platelets, and FIB-4 index. The accuracy, sensitivity, specificity, positive predictive value, and negative predictive value of the XGBoost model (cutoff value=0.5) were 99.5%, 69.7%, 99.9%, 97.4%, and 99.5%, respectively, for the entire dataset. Conclusions Machine learning algorithms effectively provide risk stratification for DAA failure and additional information on the factors associated with DAA failure.