MRI-based radiomics signatures for preoperative prediction of Ki-67 index in primary central nervous system lymphoma.

PURPOSE: The aim of this study was to develop and validate radiomics signatures based on MRI for preoperative prediction of Ki-67 proliferative index (PI) expression in primary central nervous system lymphoma (PCNSL). METHODS: A total of 341 patients with PCNSL were retrospectively analyzed, including 286 patients in one center as the training set and 55 patients in another two centers as the external validation set. Radiomics features were extracted and selected from preoperative contrast-enhanced T1-weighted images, fluid attenuation inversion recovery to build radiomics signatures according to the Ki-67 PI. The predictive performances of the radiomics model were evaluated using four classifiers including random forest, K-Nearest Neighbors, Neural Network and Decision Tree. A combined model was built by incorporating radiomics signature, clinical variables and MRI radiological characteristics using multivariate logistic regression analysis, and a nomogram was established to predict the expression of Ki-67 individually. The predictive performances of the models were evaluated using area under receiver operating characteristic curve (AUC) and decision curve analysis (DCA). RESULTS: Radiomics signatures were independent predictors of the expression level of Ki-67 (OR: 2.523, P < 0.001). RF radiomics models had the highest accuracy (0.934 in the training set and 0.811 in the external validation set) and F1 Score (0.920 in the training set and 0.836 in the external validation set). The clinic-radiologic-radiomics nomogram showed better predictive performance with AUCs of 0.877(95 % CI: 0.837-0.918) in the training set and 0.866(95 % CI: 0.774-0.957) in the external validation set. The calibration curve and DCA demonstrated goodness-of-fit and improved benefits in clinical practice of the nomogram. CONCLUSIONS: Nomograms integrating MRI-based radiomics and clinical-radiological characteristics could effectively predict Ki-67 PI in primary PCNSL.

Real-world experience of teriflunomide in relapsing multiple sclerosis: paramagnetic rim lesions may play a role.

Objectives: The aims of this study were to report the effectiveness and safety of teriflunomide in Chinese patients with relapsing-remitting multiple sclerosis (RRMS) and to explore the association of paramagnetic rim lesion (PRL) burden with patient outcome in the context of teriflunomide treatment and the impact of teriflunomide on PRL burden. Methods: This is a prospective observational study. A total of 100 RRMS patients treated with teriflunomide ≥3 months were included in analyzing drug persistence and safety. Among them, 96 patients treated ≥6 months were included in assessing drug effectiveness in aspects of no evidence of disease activity (NEDA) 3. The number and total volume of PRL were calculated in 76 patients with baseline susceptibility-weighted imaging (SWI), and their association with NEDA3 failure during teriflunomide treatment was investigated. Results: Over a treatment period of 19.7 (3.1-51.7) months, teriflunomide reduced annualized relapse rate (ARR) from 1.1 ± 0.8 to 0.3 ± 0.5, and Expanded Disability Status Scale (EDSS) scores remained stable. At month 24, the NEDA3% and drug persistence rate were 43.8% and 65.1%, respectively. In patients with a baseline SWI, 81.6% had at least 1 PRL, and 42.1% had ≥4 PRLs. The total volume of PRL per patient was 0.3 (0.0-11.5) mL, accounting for 2.3% (0.0%-49.0%) of the total T2 lesion volume. Baseline PRL number ≥ 4 (OR = 4.24, p = 0.009), younger onset age (OR = 0.94, p = 0.039), and frequent relapses in initial 2 years of disease (OR = 13.40, p = 0.026) were associated with NEDA3 failure. The PRL number and volume were not reduced (p = 0.343 and 0.051) after teriflunomide treatment for more than 24 months. No new safety concerns were identified in this study. Conclusion: Teriflunomide is effective in reducing ARR in Chinese patients with RRMS. Patients with less PRL burden, less frequent relapses, and relatively older age are likely to benefit more from teriflunomide, indicating that PRL might be a valuable measurement to inform clinical treatment decision.