Résumé
Objective:To establish a radiomics-based biomarker for predicting pathological response after preoperative neoadjuvant chemoradiotherapy (nCRT) in locally advanced esophageal cancer.Methods:From 2008 to 2018, 112 patients with locally advanced esophageal cancer who received nCRT were enrolled. All patients were treated with preoperative nCRT combined with surgery. Enhanced CT images and clinical information before nCRT were collected. A lesion volume of interest was manually delineated. In total, 670 radiomics features (including tumor intensity, shape and size, texture and wavelet characteristics) were extracted using the pyradiomics package in PYTHON. The stepwise regression combined with the best subset were employed to select the features, and finally the Logistic regression model was adopted to establish the prediction model. The performance of the classifier was evaluated by the area under the ROC curve (AUC). Results:The pathological complete remission (pCR) rate was 58.0%(65/112). 10 radiomics features were included in the final model, The most relevant radiomics feature was the gray feature (the texture information of the image), followed by the shape and voxel intensity-related features. In the training set, the AUC was 0.750 with a sensitivity of 0.711 and a specificity of 0.778, the corresponding values in the testing set were 0.870, 0.757 and 0.900, respectively.Conclusions:Models based on radiomics features from CT images can be utilized to predict the pathological response to nCRT in esophageal cancer. As it is efficient, non-invasive and economic model, it could serve as a promising tool for individualized treatment when validated by further prospective trials in the future.
Résumé
Objective:To analyze the relationship between hematological examination combined with clinical factors and radiation-induced hypothyroidism (HT) in patients with nasopharyngeal carcinoma (NPC).Methods:Clinical data of 206 patients with NPC who received radiotherapy in Cancer Hospital of University of Chinese Academy of Sciences between January 2015 to August 2018 were retrospectively analyzed. The correlation between general clinical data, hematological examination and HT was analyzed to establish a hematological prediction model.Results:Univariate analysis showed that sex, N-stage, thyroid volume, average dose, V 20Gy, V 25Gy, V 30Gy, V 35Gy, V 40Gy, V 45Gy, fibrinogen content, cholinesterase and neutrophil count were closely associated with the incidence of HT. Multivariate analysis showed that thyroid volume, fibrinogen content and cholinesterase were the independent predictors of HT. Conclusion:The combination of sex, N-stage, thyroid volume, dose parameters, fibrinogen content, cholinesterase, neutrophil percentage and neutrophil count can predict the incidence of HT (AUC=0.777).