Paraquat poisoning has become a serious public health problem in some Asian countries because of misuse or suicide. We sought to develop and validate a radiomics nomogram incorporating radiomics signature and laboratory bio-markers, for differentiating bacterial pneumonia and acute paraquat lung injury. 180 patients with pneumonia and acute paraquat who underwent CT examinations between December 2014 and October 2017 were retrospectively evaluated for testing and validation. Clinical information including demographic data, clinical symptoms and laboratory test were also recorded. A prediction model was built by using backward logistic regression and presented on a nomogram. The radiomics-based features yielded areas under the receiver operating characteristic curve of 0.870 (95% CI 0.757–0.894), sensitivity of 0.857, specificity of 0.804, positive predictive value of 83.3%, negative predictive value of 0.818 in the primary cohort, while in the validation cohort the model showed similar results (0.865 (95% CI 0.686–0.907), 0.833, 0.792, 81.5%, respectively). The individualized nomogram included radiomics signature, body temperature, nausea and vomiting, and aspartate transaminase. We have developed a radiomics nomogram that combination of the radiomics features and clinical risk factors to differentiate paraquat lung injury and pneumonia for patients with an unclear medical history of exposure to paraquat poisoning, providing appropriate therapy decision support.

3 Technical Efficacy: Stage 5 J. Magn. Reson. Imaging 2018;48:964-970.

Objective. To evaluate the efficiency of a radiomics model in predicting the prognosis of patients with acute paraquat poisoning (APP). Materials and Methods. Chest computed tomography images and clinical data of 80 patients with APP were obtained from November 2014 to October 2017, which were randomly assigned to a primary group and a validation group by a ratio of 7 : 3, and then the radiomics features were extracted from the whole lung. Principal component analysis (PCA) and least absolute shrinkage and selection operator (LASSO) regression were used to select the features and establish the radiomics signature (Rad-score). Multivariate logistic regression analysis was used to establish a radiomics prediction model incorporating the Rad-score and clinical risk factors; the model was represented by nomogram. The performance of the nomogram was confirmed by its discrimination and calibration. Result. The area under the ROC curve of operation was 0.942 and 0.865, respectively, in the primary and validation datasets. The sensitivity and specificity were 0.864 and 0.914 and 0.778 and 0.929, and the prediction accuracy rates were 89.5% and 87%, respectively. Predictors included in the individualized predictive nomograms include the Rad-score, blood paraquat concentration, creatine kinase, and serum creatinine. The AUC of the nomogram was 0.973 and 0.944 in the primary and validation datasets, and the sensitivity and specificity were 0.943 and 0.955, respectively, in the primary dataset and 0.889 and 0.929 in the validation dataset, and the prediction accuracy was 94.7% and 91.3%, respectively. Conclusion. The radiomics nomogram incorporates the radiomics signature and hematological laboratory data, which can be conveniently used to facilitate the individualized prediction of the prognosis of APP patients.

The unmeasurable enhancement patterns in HGG patients within 1 month after gross-total resection, which might be better than the grade of tumor, holds a potential marker in survival state.

Fast cine phase contrast magnetic resonance angiography (PC-MRA) has the potential to provide a quantitative measurement method for the diagnosis and treatment of cerebrovascular disease. To evaluation the changes of cerebral blood flow and the characteristics of artery lesion distribution in the patients of transient ischemic attacks (TIA). In all, 98 normal subjects and 106 TIA patients who underwent MRI examination within 72 h after the last symptom onset including the DWI sequence to exclude acute cerebral infarction were enrolled. The blood flow of the cranial total, the area of the internal carotid artery and vertebral artery, the average velocity, and the average blood flow were obtained and compared in normal subjects and TIA group. Analysis of Variance (ANOVA), t-test, and Kruskal-Wallis test were used for statistical assessments. The total cerebral blood flow of the TIA group and normal control group was no significant statistical difference (P>0.05). The total blood flow decreased with increasing age, and the TIA group was much lower than the control group. The blood flow of the right internal carotid artery in the TIA group had a significant difference compared with controls (P<0.05). However, the same situation did not happen in both of the left internal carotid artery and vertebral artery. Phase contrast magnetic resonance imaging has the potential to evaluate the change of cerebral blood flow in TIA patients. The decrease in the total blood flow and the symptom onset of TIA is consistent. Phase contrast magnetic resonance imaging could provide guidance to the diagnosis of TIA.