https://www.selleckchem.com/products/azd7545.html 67; 95% CI 0.54-0.84, P=0.0006). The addition of TRT significantly improved OS in patients over 65 years of age (HR =0.55; 95% CI 0.40-0.74, P=0.0001). For patients with only one organ metastasis, there was no significant difference in OS between the two groups (HR =0.61; 95% CI 0.36-1.01, P=0.06). There was no statistical difference in hematologic toxicity (leukopenia, thrombocytopenia, anemia) and non-hematologic toxicity (nausea or vomiting) between the two groups. The incidence of grade ≥3 esophageal toxicity was 4.6% in the TRT group and 0% in the non-TRT group (P=0.0001). Grade ≥3 bronchopulmonary toxicity was 2.9% in the TRT group and 0.8% in the non-TRT group (P=0.02). TRT improves OS, PFS and LRFS in patients with ES-SCLC, with a low increase in esophageal and bronchopulmonary toxicity. More randomized controlled trials (RCTs) are expected to confirm our conclusions. CRD42020190575. CRD42020190575. To investigate the feasibility of integrating global radiomics and local deep features based on multi-modal magnetic resonance imaging (MRI) for developing a noninvasive glioma grading model. In this study, 567 patients [211 patients with glioblastomas (GBMs) and 356 patients with low-grade gliomas (LGGs)] between May 2006 and September 2018, were enrolled and divided into training (n=186), validation (n=47), and testing cohorts (n=334), respectively. All patients underwent postcontrast enhanced T1-weighted and T2 fluid-attenuated inversion recovery MRI scanning. Radiomics and deep features (trained by 8,510 3D patches) were extracted to quantify the global and local information of gliomas, respectively. A kernel fusion-based support vector machine (SVM) classifier was used to integrate these multi-modal features for grading gliomas. The performance of the grading model was assessed using the area under receiver operating curve (AUC), sensitivity, specificity, Delong test, and -test. The AUC, sensitivity, and spec