Malignant transformation arising in mature cystic teratoma (MT-MCT) is a rare neoplasm of the ovary. Herein, we aimed to evaluate the clinicopathological features and treatment outcome of the Han Chinese women with MT-MCT. In this retrospective study, the clinical data of patients who had been surgically treated from January 2000 to November 2019 and in whom the diagnosis of MCT was confirmed based on the pathology were included. Fourteen patients with MT-MCT from a total of 569 cases (2.46% incidence) of MCT were reviewed. The mean age of patients with MT-MCT was 51.3 (range, 31–71) years, while the mean age of patients with MCT was 45.3 (range, 17–62) years. Upon gross examination, the mean size of MT-MCT was 14.0 (range, 11–25) cm, whereas the mean size of MCT was 7.5 (range, 4–10) cm. Primary surgical staging was performed in all cases. Complete cytoreduction and suboptimal surgical resection were performed in 12 (85.7%) and 2 (14.3%) cases, respectively. Thirteen patients with malignant transformation of squamous cell carcinoma (SCC) whose Federation International of Gynecology and Obstetrics stage was >1 received chemotherapy, comprising carboplatin and paclitaxel. Response to the chemotherapy regimen was complete in 12 patients; 1/12 patients died within the median follow-up period of 16.5 months. The 5-year overall survival rate and disease-free survival rates were 31.2% and 31.6%, respectively. From the data generated, we conclude that the rate of MT-MCT increases with age. The MT-MCT was much higher in women of postmenopausal age than in younger women. We described our experience of successfully treating patients with malignant transformation of SCC with primary surgical staging and adjuvant chemotherapy (cisplatin, paclitaxel, bleomycin, and etoposide) that might improve survival in patients with advanced-stage disease.

This study aimed to clarify the association between serum total cholesterol (TC) levels and overall cancer risk. Study-specific relative risks (RR) and 95% confidence intervals (CI) were pooled using a random-effects model, and dose–response relation was also evaluated. Twelve prospective studies were identified with a total of 1,926,275 participants and 13,1676 cases. High levels of serum TC showed an inverse association with overall cancer risk (RR for the highest versus the lowest category: 0.87, 95% CI: 0.83 ∼ 0.90; I² = 52.5%). A linear dose-response relation between serum TC levels and overall cancer risk was found (p = .004 for Wald test; I² = 49.6%), and the pooled RR was 0.92 (95% CI: 0.89 ∼ 0.94) for 3 mmol/L, 0.86 (95% CI: 0.81 ∼ 0.90) for 5 mmol/L, 0.80 (95% CI: 0.74 ∼ 0.87) for 7 mmol/L. Our dose-response meta-analysis of 12 prospective studies indicated that higher serum TC levels were significantly associated with reduced cancer risk.

Dietary fat intake and risk of esophageal carcinoma: a meta-analysis of observational studies Dietary fat intake and risk of esophageal carcinoma: a meta-analysis of observational studies

Purpose: Deep myometrial invasion (DMI) is an independent high-risk factor for lymph node metastasis and a prognostic risk factor in early-stage endometrial cancer (EC-I) patients. Thus, we developed a machine learning (ML) assistant model, which can accurately help define the surgical area. Methods: 348 consecutive EC-I patients with the pathological diagnosis were recruited in the tertiary medical centre between January 1, 2012, and October 31, 2021. Five ML-assisted models were developed using two-step estimation methods from the candidate gray level co-occurrence matrix (GLCM). Receiver operating characteristic curve (ROC), decision curve analysis (DCA), and clinical impact curve (CIC) were prepared to evaluate the robustness and clinical practicality of each model. Results: Our analysis identified several significant differences between the stage IA and IB groups. The top seven-candidate factors included correlation all direction offset1, correlation angle0 offset1, correlation angle45 offset1, correlation angle90 offset1, ID moment all direction offset1, ID moment angle0 offset1, and ID moment angle45 offset1. The areas under the ROC curve (AUCs) of the random forest classifier (RFC) model, support vector machine (SVM), eXtreme gradient boosting (XGBoost), artificial neural network (ANN), and decision tree (DT) ranged from 0.765 to 0.877 in the training set and from 0.716 to 0.862 in the testing set, respectively. Among the five machine algorithms, RFC obtained the optimal prediction efficiency using correlation angle0 offset1, correlation angle45 offset1, correlation angle90 offset1, correlation all direction offset1, ID moment angle0 offset1, and ID moment angle45 offset1, and ID moment angle90 offset1, respectively. Conclusion: Our ML-based prediction model combined with GLCM parameters assessed the risk of DMI in EC-I patients, especially RFC, which helped distinguish stage IA and IB EC patients. This new predictive model based on supervised learning can be used to establish personalized treatment strategies.