Differentiation Between Benign and Nonbenign Meningiomas by Using Texture Analysis From Multiparametric MRI.

BACKGROUND: It is difficult to prospectively differentiate between benign (World Health Organization [WHO] I) and nonbenign (WHO II and III) meningiomas. PURPOSE: To evaluate the feasibility of preoperative differentiation between benign and nonbenign meningiomas by using texture analysis from multiparametric MR data. STUDY TYPE: Retrospective. SUBJECTS: In all, 184 patients with meningioma (139 benign and 45 nonbenign) were included as the training cohort and 79 patients with meningioma (60 benign and 19 nonbenign) were included as the external validation cohort. FIELD STRENGTH/SEQUENCE: T1 -weighted, T2 -weighted, and contrast-enhanced T1 -weighted imaging were performed on 1.5 or 3.0T MR systems from two centers. ASSESSMENT: Tumor segmentation and radiological characteristic (RC) evaluation were performed by experienced radiologists. The texture features were extracted from preprocessed images and combined with RCs, and then the combined features were reduced by using a two-step feature selection. Three single-sequence models and a multiparametric MRI (the combination of single sequences) model were constructed and then evaluated with the external validation cohort. STATISTICAL TESTS: Area under receiver operating characteristic curve (AUC), accuracy (Acc), f1-score (F1), sensitivity (Sen), and specificity (Spec), were calculated to quantify the performance of the models. RESULTS: Among the four texture models, the multiparametric MRI model demonstrated the best performance for differentiating between benign and nonbenign meningiomas in both the training and external validation cohorts (AUC 0.91, Acc 89%, F1 0.88, Sen 0.93, and Spec 0.87 in the training cohort; AUC 0.83, Acc 80%, F1 0.77, Sen 0.84, and Spec 0.78 in the validation cohort). DATA CONCLUSION: Nonbenign meningiomas might be preoperatively differentiated from benign meningiomas by using texture analysis from multiparametric MR data. LEVEL OF EVIDENCE: 3 Technical Efficacy Stage: 2 J. Magn. Reson. Imaging 2020;51:1810-1820.

Radiomics analysis of multiparametric MRI for the preoperative evaluation of pathological grade in bladder cancer tumors.

OBJECTIVES: To develop and validate an MRI-based radiomics strategy for the preoperative estimation of pathological grade in bladder cancer (BCa) tumors. METHODS: A primary cohort of 70 patients (31 high-grade BCa and 39 low-grade BCa) with BCa were retrospectively enrolled. Three sets of radiomics features were separately extracted from tumor volumes on T2-weighted imaging (T2WI), diffusion-weighted imaging (DWI), and apparent diffusion coefficient (ADC) maps. Two sets of multimodal features were separately generated by the maxout and concatenation of the above mentioned single-modality features. Each feature set was subjected to a two-sample t test and the least absolute shrinkage and selection operator (LASSO) algorithm for feature selection. Multivariable logistic regression (LR) analysis was used to obtain five corresponding radiomics models. The diagnostic abilities of the radiomics models were evaluated using receiver operating characteristic (ROC) curve analysis and compared using the DeLong test. Validation was performed on a time-independent cohort containing 30 consecutive patients. RESULTS: The areas under the ROC curves (AUCs) of single-modality T2WI, DWI, and ADC models in the training cohort were 0.7933 (95% confidence interval [CI] 0.7471-0.8396), 0.8083 (95% CI 0.7565-0.8601), and 0.8350 (95% CI 0.7924-0.8776), respectively. Both multimodality models achieved higher AUCs (maxout 0.9233, 95% CI 0.9001-0.9466; concatenation 0.9233, 95% CI 0.9001-0.9466) than single-modality models. The AUCs of the maxout and concatenation models in the validation cohort were 0.9186 and 0.9276, respectively. CONCLUSIONS: The MRI-based multiparametric radiomics approach has the potential to be used as a noninvasive imaging tool for preoperative grading of BCa tumors. Multicenter validation is needed to acquire high-level evidence for its clinical application. KEY POINTS: • Multiparametric MRI may help in the preoperative grading of BCa tumors. • The Joint_Model established from T2WI, DWI, and ADC feature subsets demonstrated a high diagnostic accuracy for preoperative prediction of pathological grade in BCa tumors. • The radiomics approach has the potential to preoperatively assess tumor grades in BCa and avoid subjectivity.

Late-responding normal tissue cells benefit from high-precision radiotherapy with prolonged fraction delivery times via enhanced autophagy.

High-precision radiotherapy (HPR) has established its important role in the treatment of tumors due to its precise dose distribution. Given its more complicated delivery process, HPR commonly requires more fraction delivery time (FDT). However, it is unknown whether it has an identical response of prolonged FDT on different normal tissues. Our results showed that fractionated irradiation with prolonged FDTs (15, 36, and 50 minutes) enhanced cell surviving fractions for normal tissue cells compared with irradiation with an FDT of 2 minutes. However, the late-responding normal cell line HEI-OC1 was more responsive to prolonged FDTs and demonstrated higher surviving fractions and significantly decreased apoptosis and DNA damage compared to the acute-responding normal cell line HaCaT. Increased autophagy mediated via the ATM-AMPK pathway was observed in HEI-OC1 cells compared with HaCaT cells when irradiated with prolonged FDTs. Furthermore, treatment with the autophagy inhibitor 3-MA or ATM inhibitor KU55933 resulted in enhanced ROS accumulation and attenuation of the effect of prolonged FDT-mediated protection on irradiated HEI-OC1 cells. Our results indicated that late-responding normal tissue cells benefitted more from prolonged FDTs compared with acute-responding tissue cells, which was mainly attributed to enhanced cytoprotective autophagy mediated via the ATM/AMPK signaling pathway.