ObjectiveThis study was conducted to develop and validate a radiomics-clinics combined model-based magnetic resonance imaging (MRI) radiomics and clinical features for the early prediction of radiation-induced temporal lobe injury (RTLI) in patients with nasopharyngeal carcinoma (NPC).MethodsThis retrospective study was conducted using data from 130 patients with NPC (80 patients with and 50 patients without RTLI) who received radiotherapy. Cases were assigned randomly to training (n = 91) and testing (n = 39) datasets. Data on 168 medial temporal lobe texture features were extracted from T1WI, T2WI, and T1WI-CE MRI sequences obtained at the end of radiotherapy courses. Clinics, radiomics, and radiomics–clinics combined models (based on selected radiomics signatures and clinical factors) were constructed using machine learning software. Univariate logistic regression analysis was performed to identify independent clinical factors. The area under the ROC curve (AUC) was performed to evaluate the performance of three models. A nomogram, decision curves, and calibration curves were used to assess the performance of the combined model.ResultsSix texture features and three independent clinical factors associated significantly with RTLI were used to build the combined model. The AUCs for the combined and radiomics models were 0.962 [95% confidence interval (CI), 0.9306–0.9939] and 0.904 (95% CI, 0.8431–0.9651), respectively, for the training cohort and 0.947 (95% CI, 0.8841–1.0000) and 0.891 (95% CI, 0.7903–0.9930), respectively, for the testing cohort. All of these values exceeded those for the clinics model (AUC = 0.809 and 0.713 for the training and testing cohorts, respectively). Decision curve analysis showed that the combined model had a good corrective effect.ConclusionThe radiomics–clinics combined model developed in this study showed good performance for predicting RTLI in patients with NPC.

In this article, the authors explore the relationships between the geographical distribution of population, potential political redistricting schemes, and the resulting level of minority representation. The maximal fraction of districts that are minority districts is twice the fraction of minorities in the total population. The authors show how this maximum declines with increasing residential segregation. Finally, they provide some simulation results for both hypothetical cities and Buffalo council wards showing the degree of minority representation under random districting plans. This gives a baseline indicator of how population distribution is expected to influence representation and can then be compared with actual plans.

Background: Antiangiogenic therapy may transiently “normalize” the tumor vasculature to increase the effect of chemoradiotherapy. The present study confirmed the combination of antiangiogenic therapy with chemoradiotherapy provides a promising strategy for the improvement of the prognosis of nasopharyngeal carcinoma(NPC) patients. Recombinant human endostatin (RHES) is a new type of antiangiogenic agent. Radiomics is a new method to study tumor heterogeneity, and it can evaluate tumor heterogeneity and biological characteristics by screening these features and constructing models. This study was aimed at investigating the capability of an MRI radiomics model based on pre-treatment texture features in predicting the efficacy of RHES + concurrent chemoradiotherapy (CCRT) for NPC.Methods:In total, we retrospectively enrolled 65 patients newly diagnosed as having NPC and treated with RHES + CCRT. 144 texture features were extracted from the MRI before RHES+CCRT treatment of all the NPC patients. The maximum relevance minimum redundancy (mRMR) method was used to remove redundant, irrelevant texture features, and calculate the Rad score of the primary tumor. Multivariable logistic regression was used to select the most predictive features subset, and prediction models were constructed, which included radiomics models, clinics models, and combined models. The performance of the three models in predicting the early response of RHES + CCRT for NPC was explored.Result:The diagnostic efficiency of combined model and radiomics model in distinguishing between the groups was found to be moderate. The area under the curve (AUC) of the combined model and radiomics model was 0.74 (95% CI: 0.62–0.86) and 0.71 (95% CI: 0.58–0.84), respectively, with both being higher than the AUC of the clinics model (0.63, 95% CI: 0.49–0.78). Compared with the pure radiomics model, the combined model showed marginally improved diagnostic performance in predicting RHES + CCRT treatment response. The accuracy of combined model and radiomics model for RHES + CCRT response assessment in NPC were higher than those of the clinics model (0.723, 0.723 vs. 0.677). Conclusion:The pretreatment MRI based radiomics can predict RHES + CCRT response in patients with NPC with a better accuracy than the clinics model.