Contrast-enhanced CT based radiomics in the preoperative prediction of perineural invasion for patients with gastric cancer. Issue 154 (September 2022)

• Record Type: Journal Article
• Authors: Zheng, Haoze; Zheng, Qiao; Jiang, Mengmeng; Han, Ce; Yi, Jinling; Ai, Yao; Xie, Congying; Jin, Xiance
• Is Part Of: European journal of radiology. Issue 154(2022)
• Publication Date: 2022-09
• Subjects: Gastric cancer -- Perineural invasion -- Contrast-enhanced CT -- Radiomics -- Area under curve; AUC Area under the curve -- ROC Receiver-Operating-Characteristic -- PNI Perineural invasion -- GC Gastric cancer -- CECT Contrast-enhanced CT -- LASSO Least absolute shrinkage and selection operator -- ICC Inter-class correlation coefficient -- SVM Support vector machines -- R Radiomics features -- C Clinical factors -- R+C Radiomics features and clinical factors -- OS Overall survival -- ML Machine learning; Medical radiology -- Periodicals; Radiology -- Periodicals; Radiologie médicale -- Périodiques; Medical radiology; Periodicals; 616.075705
• DOI: 10.1016/j.ejrad.2022.110393
• Languages: English
• ISSNs: 0720-048X

Abstract

Highlights: CECT radiomics to predict perineural invasion for gastric cancer preoperatively; Models based on both radiomics features and clinical factors. Combined models achieved a reasonable AUC and accuracy for PNI prediction. Radiomics is promising to classify and improve the management GC. Abstract: Purpose: To investigate the feasibility and accuracy of radiomics models based on contrast-enhanced CT (CECT) in the prediction of perineural invasion (PNI), so as to stratify high-risk recurrence and improve the management of patients with gastric cancer (GC) preoperatively. Methods: Total of 154 GC patients underwent D2 lymph node dissection with pathologically confirmed GC and preoperative CECT from an open-label, investigator-sponsored trial (NCT01711242) were enrolled. Radiomics features were extracted from contoured images and selected using Mann-Whitney U test and the least absolute shrinkage and selection operator (LASSO) after inter-class correlation coefficient (ICC) analysis. Models based on radiomics features (R), clinical factors (C) and combined parameters (R + C) were built and evaluated using Support Vector Machine (SVM) and logistic regression to predict the PNI for patients with GC preoperatively. Results: Total of 11 radiomics features were selected for final analysis, along with two clinical factors. The area under curve (AUC) of models based on R, C, and R + C with logistic regression and SVM were 0.77 vs. 0.83, 0.71 vs.0.70, 0.86 vs. 0.90, and 0.73 vs.0.80, 0.62 vs. 0.64, 0.77 vs. 0.82 in the training and testing cohorts, respectively. SVM(R + C) achieved a best AUC of 0.82(0.69–0.94) in the test cohorts with a sensitivity, specificity and accuracy of 0.63, 0.91, and 0.77, respectively. Conclusions: The performance of these models indicates that radiomics features alone or combined with clinical factors provide a feasible way to classify patients preoperatively and improve the management of patients with GC.