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Purpose: To develop a radiomics-based model from preoperative magnetic resonance imaging (MRI) for predicting the histopathological grades of myxofibrosarcoma. Methods: This retrospective study included 54 patients. The tumors were classified into high-grade and low-grade myxofibrosarcoma. The tumor size, signal intensity heterogeneity, margin, and surrounding tissue were evaluated on MRI. Using the least absolute shrinkage and selection operator (LASSO) algorithms, 1037 radiomics features were obtained from fat-suppressed T2-weighted images (T2WI), and a radiomics signature was established. Using multivariable logistic regression analysis, three models were built to predict the histopathologic grade of myxofibrosarcoma. A radiomics nomogram represents the integrative model. The three models' performance was evaluated using the receiver operating characteristics (ROC) and calibration curves. Results: The high-grade myxofibrosarcoma had greater depth (P = 0.027), more frequent heterogeneous signal intensity at T2WI (P = 0.015), and tail sign (P = 0.014) than the low-grade tumor. The area under curve (AUC) of these conventional MRI features models was 0.648, 0.656, and 0.668, respectively. Seven radiomic features were selected by LASSO to construct the radiomics signature model, with an AUC of 0.791. The AUC of the integrative model based on radiomics signature and conventional MRI features was 0.875. The integrative model's calibration curve and insignificant Hosmer–Lemeshow test statistic (P = 0.606) revealed good calibration. Conclusion: An integrative model using radiomics signature and three conventional MRI features can preoperatively predict low- or high-grade myxofibrosarcoma. [ABSTRACT FROM AUTHOR]

To explore the relationship between circular RNA (circRNA) in gestational diabetes mellitus (GDM) and the metabolic profile at the molecular level, and find a biological marker that can predict GDM early. A retrospective case–control study was conducted using data and samples from women treated at a hospital in China between January 10 2018 and February 20 2019. Reverse transcription polymerase chain reaction (qRT-PCR) was used to evaluate the expression level of hsa_circRNA_102682 in serum and analyze its correlation with lipid metabolism parameters. Advanced age and higher pre-pregnancy body mass index (BMI) during pregnancy are risk factors for GDM. The expression level of hsa_circ_102682 was lower among the cases than the controls (p=.000). The levels of triglyceride, apolipoprotein A1 (APOA1), APOB, and high-density lipoprotein cholesterol (HDL-C) were different between the controls and cases (p<.05). Hsa_circRNA_102682 was significantly correlated with triglycerides, APOA1, APOB, 1-h blood glucose in the serum of GDM patients, and the correlation coefficients were 0.319, 0.314, 0.286, and 0.311, respectively (p<.05). The area under the receiver operating characteristic curve is 0.684 (95% confidence interval 0.611–0.756, p=.0001). Hsa_circRNA_102682 may regulate lipid metabolism, participate in the pathogenesis of GDM. It can be used as a marker to predict GDM. [ABSTRACT FROM AUTHOR]