Objective:Radiotherapy is the primary treatment for nasopharyngeal carcinoma,but it frequently leads to radiotherapy-induced temporal lobe injury(RTLI).Magnetic resonance imaging(MRI)is the main diagnostic method for RTLI after radiotherapy for nasopharyngeal carcinoma,but it is prone to missed diagnoses.This study aims to investigate the causes of missed diagnoses of RTLI in nasopharyngeal carcinoma patients undergoing MRI after radiotherapy. Methods:Clinical and MRI data from nasopharyngeal carcinoma patients diagnosed and treated with radiotherapy at Xiangya Hospital of Central South University,from January 2010 to April 2021,were collected.Two radiologists reviewed all head and neck MRIs(including nasopharyngeal and brain MRIs)before and after radiotherapy of identify cases of late delayed response-type RTLI for the first time.If the original diagnosis of the initial RTLI in nasopharyngeal carcinoma patients did not report temporal lobe lesions,it was defined as a missed diagnosis.The first diagnosis of RTLI cases was divided into a missed diagnosis group and a non-missed diagnosis group.Clinical and imaging data were compared between the 2 groups,and multivariate logistic regression analysis was used to identify independent risk factors for MRI missed diagnoses of initial RTLI. Results:A total of 187 nasopharyngeal carcinoma with post-radiotherapy RTLI were included.The original diagnostic reports missed 120 cases and accurately diagnosed 67 cases,with an initial RTLI diagnosis accuracy rate of 35.8%and a missed diagnosis rate of 64.2%.There were statistically significant differences between the missed diagnosis group and the non-missed diagnosis group in terms of lesion size,location,presence of contralateral temporal lobe lesions,white matter high signal,cystic degeneration,hemorrhage,fluid attenuated inversion recovery(FLAIR),and examination site(all P<0.05).Multivariate logistic regression analysis showed that lesions≤25 mm,non-enhancing lesions,lesions without cystic degeneration or hemorrhage,lesions located only in the medial temporal lobe,and MRI examination only of the nasopharynx were independent risk factors for missed MRI diagnosis of initial RTLI(all P<0.05). Conclusion:The missed diagnosis of initial RTLI on MRI is mainly related to lesion size and location,imaging characteristics,and MRI examination site.

Objective: To develop a machine learning (ML) pipeline based on radiomics to predict Coronavirus Disease 2019 (COVID-19) severity and the future deterioration to critical illness using CT and clinical variables. Materials and Methods: Clinical data were collected from 981 patients from a multi-institutional international cohort with real-time polymerase chain reaction-confirmed COVID-19. Radiomics features were extracted from chest CT of the patients. The data of the cohort were randomly divided into training, validation, and test sets using a 7:1:2 ratio. A ML pipeline consisting of a model to predict severity and time-to-event model to predict progression to critical illness were trained on radiomics features and clinical variables. The receiver operating characteristic area under the curve (ROC-AUC), concordance index (C-index), and time-dependent ROC-AUC were calculated to determine model performance, which was compared with consensus CT severity scores obtained by visual interpretation by radiologists. Results: Among 981 patients with confirmed COVID-19, 274 patients developed critical illness. Radiomics features and clinical variables resulted in the best performance for the prediction of disease severity with a highest test ROC-AUC of 0.76 compared with 0.70 (0.76 vs. 0.70, p = 0.023) for visual CT severity score and clinical variables. The progression prediction model achieved a test C-index of 0.868 when it was based on the combination of CT radiomics and clinical variables compared with 0.767 when based on CT radiomics features alone (p < 0.001), 0.847 when based on clinical variables alone (p = 0.110), and 0.860 when based on the combination of visual CT severity scores and clinical variables (p = 0.549). Furthermore, the model based on the combination of CT radiomics and clinical variables achieved time-dependent ROC-AUCs of 0.897, 0.933, and 0.927 for the prediction of progression risks at 3, 5 and 7 days, respectively. Conclusion CT radiomics features combined with clinical variables were predictive of COVID-19 severity and progression to critical illness with fairly high accuracy.

Objective: To develop a machine learning (ML) pipeline based on radiomics to predict Coronavirus Disease 2019 (COVID-19) severity and the future deterioration to critical illness using CT and clinical variables. Materials and Methods: Clinical data were collected from 981 patients from a multi-institutional international cohort with real-time polymerase chain reaction-confirmed COVID-19. Radiomics features were extracted from chest CT of the patients. The data of the cohort were randomly divided into training, validation, and test sets using a 7:1:2 ratio. A ML pipeline consisting of a model to predict severity and time-to-event model to predict progression to critical illness were trained on radiomics features and clinical variables. The receiver operating characteristic area under the curve (ROC-AUC), concordance index (C-index), and time-dependent ROC-AUC were calculated to determine model performance, which was compared with consensus CT severity scores obtained by visual interpretation by radiologists. Results: Among 981 patients with confirmed COVID-19, 274 patients developed critical illness. Radiomics features and clinical variables resulted in the best performance for the prediction of disease severity with a highest test ROC-AUC of 0.76 compared with 0.70 (0.76 vs. 0.70, p = 0.023) for visual CT severity score and clinical variables. The progression prediction model achieved a test C-index of 0.868 when it was based on the combination of CT radiomics and clinical variables compared with 0.767 when based on CT radiomics features alone (p < 0.001), 0.847 when based on clinical variables alone (p = 0.110), and 0.860 when based on the combination of visual CT severity scores and clinical variables (p = 0.549). Furthermore, the model based on the combination of CT radiomics and clinical variables achieved time-dependent ROC-AUCs of 0.897, 0.933, and 0.927 for the prediction of progression risks at 3, 5 and 7 days, respectively. Conclusion CT radiomics features combined with clinical variables were predictive of COVID-19 severity and progression to critical illness with fairly high accuracy.

OBJECTIVES: Glioma is the most common intracranial primary tumor in central nervous system. Glioma grading possesses important guiding significance for the selection of clinical treatment and follow-up plan, and the assessment of prognosis. This study aims to explore the feasibility of logistic regression model based on radiomics to predict glioma grading. METHODS: Retrospective analysis was performed on 146 glioma patients with confirmed pathological diagnosis from January, 2012 to December, 2018. A total of 41 radiomics features were extracted from contrast-enhanced T RESULTS: A total of 5 imaging features selected by LASSO were used to establish a logistic regression model for predicting glioma grading. The model showed good discrimination with AUC value of 0.919. Hosmer-Lemeshow test showed no significant difference between the calibration curve and the ideal curve ( CONCLUSIONS The logistic regression model using radiomics exhibits a relatively high accuracy for predicting glioma grading, which may serve as a complementary tool for preoperative prediction of giloma grading.
Brain Neoplasms/diagnostic imaging* ; Glioma/diagnostic imaging* ; Humans ; Logistic Models ; Magnetic Resonance Imaging ; ROC Curve ; Retrospective Studies

To explore the feasibility and efficacy of artificial neural network for differentiating high-grade glioma and low-grade glioma using image information.
 Methods: A total of 130 glioma patients with confirmed pathological diagnosis were selected retrospectively from 2012 to 2017. Forty one imaging features were extracted from each subjects based on 2-dimension magnetic resonance T1 weighted imaging with contrast-enhancement. An artificial neural network model was created and optimized according to the performance of feature selection. The training dataset was randomly selected half of the whole dataset, and the other half dataset was used to verify the performance of the neural network for glioma grading. The training-verification process was repeated for 100 times and the performance was averaged.
 Results: A total of 5 imaging features were selected as the ultimate input features for the neural network. The mean accuracy of the neural network for glioma grading was 90.32%, with a mean sensitivity at 87.86% and a mean specificity at 92.49%. The area under the curve of receiver operating characteristic curve was 0.9486.
 Conclusion: As a technique of artificial intelligence, neural network can reach a relatively high accuracy for the grading of glioma and provide a non-invasive and promising computer-aided diagnostic process for the pre-operative grading of glioma.
Brain Neoplasms ; diagnostic imaging ; pathology ; Glioma ; diagnostic imaging ; pathology ; Humans ; Magnetic Resonance Imaging ; Neoplasm Grading ; Neural Networks, Computer ; ROC Curve ; Retrospective Studies ; Sensitivity and Specificity