CT radiomics based on the peritumoral adipose region of gastric adenocarcinoma for preoperative prediction of lymph node metastasis.

PURPOSE: To construct and validate CT radiomics model based on the peritumoral adipose region of gastric adenocarcinoma to preoperatively predict lymph node metastasis (LNM). METHODS AND METHODS: 293 consecutive gastric adenocarcinoma patients receiving radical gastrectomy with lymph node dissection in two medical institutions were stratified into a development set (from Institution A, n = 237), and an external validation set (from Institution B, n = 56). Volume of interest of peritumoral adipose region was segmented on preoperative portal-phase CT images. The least absolute shrinkage and selection operator method and stepwise logistic regression were used to select features and build radiomics models. Manual classification was performed according to routine CT characteristics. A classifier incorporating the radiomics score and CT characteristics was developed for predicting LNM. Area under the receiver operating characteristic curve (AUC) was used to show discrimination between tumors with and without LNM, and the calibration curves and Brier score were used to evaluate the predictive accuracy. Violin plots were used to show the distribution of radiomics score. RESULTS: AUC values of radiomics model to predict LNM were 0.938, 0.905, and 0.872 in the training, internal test, and external validation sets, respectively, higher than that of manual classification (0.674, all P values < 0.01). The radiomics score of the positive LNM group were higher than that of the negative group in all sets (both P-values < 0.001). The classifier showed no improved predictive power compared with the radiomics signature alone with AUC values of 0.916 and 0.872 in the development and external validation sets, respectively. Multivariate analysis showed that radiomics score was an independent predictor. CONCLUSIONS: Radiomics model based on peritumoral adipose region could be a useful approach for preoperative LNM prediction in gastric adenocarcinoma.

Baseline CT radiomics features to predict pathological complete response of advanced esophageal squamous cell carcinoma treated with neoadjuvant chemotherapy using paclitaxel and cisplatin.

PURPOSE: To develop a CT radiomics model to predict pathological complete response (pCR) of advanced esophageal squamous cell carcinoma (ESCC) toneoadjuvant chemotherapy using paclitaxel and cisplatin. MATERIALS AND METHODS: 326 consecutive patients with advanced ESCC from two hospitals undergoing baseline contrast-enhanced CT followed by neoadjuvant chemotherapy using paclitaxel and cisplatin were enrolled, including 115 patients achieving pCR and 211 patients without pCR. Of the 271 cases from 1st hospital, 188 and 83 cases were randomly allocated to the training and test cohorts, respectively. The 55 patients from a second hospital were assigned as an external validation cohort. Region of interest was segmented on the baseline thoracic contrast-enhanced CT. Useful radiomics features were generated by dimension reduction using least absolute shrinkage and selection operator. The optimal radiomics features were chosen using support vector machine (SVM). Discriminating performance was assessed with area under the receiver operating characteristic curve (ROC) and F-1score. The calibration curves and Brier score were used to evaluate the predictive accuracy. RESULTS: Eight radiomics features were selected to create radiomics models related to pCR of advanced ESCC (P-values < 0.01 for both the training and test cohorts). SVM model showed the best performance (AUCs = 0.929, 0.868 and 0.866, F-1scores = 0.857, 0.847 and 0.737 in the training, test and external validation cohorts, respectively). The calibration curves and Brier scores indicated goodness-of-fit and its great predictive accuracy. CONCLUSION: CT radiomics models could well help predict pCR of advanced ESCC, and SVM model could be a suitable predictive model.