Early Prediction of Disease Progression in 2019 Novel Coronavirus Pneumonia Patients Outside Wuhan with CT and Clinical Characteristics

ObjectiveTo determine the predictive value of CT and clinical characteristics for short-term disease progression in patients with 2019 novel coronavirus pneumonia (NCP). Materials and Methods224 patients with confirmed 2019 novel coronavirus (COVID-19) infection outside Wuhan who had chest CT examinations were retrospectively screened. Clinical data were obtained from electronic medical records. CT images were reviewed and scored for lesion distribution, lobe and segment involvement, ground-glass opacities, consolidation, and interstitial thickening. All included patients with moderate NCP were observed for at least 14 days from admission to determine whether they exacerbated to severe NCP (progressive group) or not (stable group). CT and clinical characteristics between the two groups were compared, and multivariate logistic regression and sensitivity analyses were performed to identify the risk factors for developing severe NCP. ResultsA total of 141 patients with moderate NCP were included, of which 15 (10.6%) patients developed severe NCP during hospitalization and assigned to the progressive group. Multivariate logistic regression analysis showed that higher neutrophil-to-lymphocyte ratio (NLR) (odds ratio [OR] and 95% confidence interval [CI], 1.26 [1.04-1.53]; P = 0.018) and CT severity score (OR and 95% CI, 1.25 [1.08-1.46]; P = 0.004) on admission were independent predictors for progression to severe NCP, and sensitivity analysis confirmed the consistent results in nonimported patients but not in imported patients. However, no significant difference in lung involvement was found on CT between imported and nonimported patients (all P > 0.05). Patients who were admitted more than 4 days from symptom onset tended to have more severe lung involvement. Spearman correlation analysis showed the close association between CT severity score and inflammatory indexes (r = 0.17[~]0.47, all P < 0.05). ConclusionCT severity score was associated with inflammatory levels and higher NLR and CT severity score on admission were independent risk factors for short-term progression in patients with NCP outside Wuhan. Furthermore, early admission and surveillance by CT should be recommended to improve clinical outcomes.

Radiomics and its advances in hepatocellular carcinoma / 中南大学学报(医学版)

Liver cancer is the second leading cause of cancer-related death worldwide,so early detection and prediction for response to treatment is of great benefit to hepatocellular carcinoma (HCC) patients.Currently,needle biopsy and conventional medical imaging play a significant and basic role in HCC patients' management,while those two approaches are limited in sample error and observerdependence.Radiomics can make up for this deficiency because it is an emerging non-invasive technic that is capable of getting comprehensive information relevant to tumor situation across spatial-temporal limitation.The basic procedure for radiomics includes image acquisition,region of interest segmentation and reconstruction,feature extraction,selection and classification,and model building and performance evaluation.The current advances and potential prospect of radiomics in HCC studies are involved in diagnosis,prediction for response to treatment,prognosis evaluation and radiogenomics.

Radiomics in predicting tumor molecular marker P63 for non-small cell lung cancer / 中南大学学报(医学版)

Objective:To establish a radiomics signature based on CT images of non-small cell lung cancer (NSCLC) to predict the expression of molecular marker P63.Methods:A total of 245 NSCLC patients who underwent CT scans were retrospectively included.All patients were confirmed by histopathological examinations and P63 expression were examined within 2 weeks after CT examination.Radiomics features were extracted by MaZda software and subjective image features were defined from original non-enhanced CT images.The Lasso-logistic regression model was used to select features and develop radiomics signature,subjective image features model,and combined diagnostic model.The predictive performance of each model was evaluated by the receiver operating characteristic (ROC) curve,and compared with Delong test.Results:Of the 245 patients,96 were P63 positive and 149 were P63 negative.The subjective image feature model consisted of 6 image features.Through feature selection,the radiomics signature consisted of 8 radiomics features.The area under the ROC curves of the subjective image feature model and the radiomics signature in predicting P63 expression statue were 0.700 and 0.755,respectively,without a significant difference (P＞0.05).The combined diagnostic model showed the best predictive power (AUC=0.817,P＜0.01).Conclusion:The radiomics-based CT scan images can predict the expression status of NSCLC molecular marker P63.The combination of the radiomics features and subjective image features can significantly improve the predictive performance of the predictive model,which may be helpful to provide a non-invasive way for understanding the molecular information for lung cancer cells.