OBJECTIVE: The malignant transformation (MT) of ovarian mature cystic teratoma (MCT) to squamous cell carcinoma (SCC) is very rare. This study analyzed cases from multiple medical centers in Taiwan to investigate the clinicopathologic characteristics, treatment, and prognostic factors of this disease and reviewed related literature. METHODS: Pathological reports of 16,001 patients with primary ovarian cancer who were treated at Taiwan medical centers from 1990 to 2011 were reviewed. In total, 52 patients with MT of MCT to SCC were identified. RESULTS: Among all ovarian MCTs, the incidence of MT to SCC is 0.2%. The median age of patients was 52 years (range, 29–89 years), and the mean tumor size was 10.5 cm (range, 1–40 cm). We analyzed the patients in our study and those in the literature and determined that early identification and complete surgical resection of the tumor are essential for long-term survival. In addition, adjuvant chemotherapy or concurrent chemoradiotherapy can be used to treat this malignancy. Old age, large tumor size (≥15.0 cm), and solid components in MCTs are suitable indicators predicting the risk of MT of MCT to SCC. CONCLUSION: Similar to general epithelial ovarian cancers, the early detection of MT of MCT to SCC is critical to long-term survival. Therefore, older patients with a large tumor or those with a tumor containing a solid component in a clinically diagnosed MCT should be evaluated to exclude potential MT to SCC.
Carcinoma, Squamous Cell* ; Cell Transformation, Neoplastic ; Chemoradiotherapy ; Chemotherapy, Adjuvant ; Epithelial Cells* ; Humans ; Incidence ; Ovarian Neoplasms ; Taiwan ; Teratoma*

Objective: The real-world INFORM study analyzed sociodemographics, treatment patterns and clinical outcomes for patients with newly diagnosed advanced epithelial ovarian cancer (EOC) in Australia, South Korea (S.Korea) and Taiwan preceding incorporation of poly(ADP-ribose) polymerase inhibitors into clinical practice. Methods: Retrospective data from patients diagnosed with EOC (high-grade serous EOC for Taiwan) between January 2014 and December 2018 with ≥12 months follow-up from diagnosis were analyzed descriptively. Survival was evaluated by Kaplan-Meier with two-sided 95% confidence interval (CI). Results: Of the 987 patients (Australia, 223; S.Korea, 513; Taiwan, 251), 98% received platinum-based chemotherapy (CT). In S.Korea and Taiwan 76.0% and 78.9% respectively underwent primary cytoreductive surgery; in Australia, 56.5% had interval debulking surgery. Bevacizumab was included in primary/maintenance therapy for 22.4%, 14.6% and 6.8% of patients in Australia, S.Korea and Taiwan, respectively. Patients receiving bevacizumab were high-risk (reimbursement policy) and achieved similar real-world progression-free survival (PFS) compared with CT only. Overall, the median real-world PFS (months; 95% CI) was similar across Australia (16.0 [14.63–18.08]), S.Korea (17.7 [16.18–19.27]) and Taiwan (19.1 [17.56–22.29]). Conclusion This study reveals poor prognosis despite differences in demographics and treatment patterns for patients with EOC across Asia-Pacific suggesting the need for biomarker-driven novel therapies to improve outcomes.

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.