Radiomics signature of computed tomography imaging for prediction of survival and chemotherapeutic benefits in gastric cancer

• Published in: EBioMedicine 2018-10, Vol.36, p.171-182
• Main Authors: Jiang, Yuming, Chen, Chuanli, Xie, Jingjing, Wang, Wei, Zha, Xuefan, Lv, Wenbing, Chen, Hao, Hu, Yanfeng, Li, Tuanjie, Yu, Jiang, Zhou, Zhiwei, Xu, Yikai, Li, Guoxin
• Format: Article
• Language: English
• Subjects: Adult; Aged; Antineoplastic Combined Chemotherapy Protocols - therapeutic use; Biomarkers; Chemotherapy; Comorbidity; Female; Gastric cancer; Humans; Image Processing, Computer-Assisted; Male; Middle Aged; Neoplasm Grading; Neoplasm Staging; Prognosis; Radiomics signature; Reproducibility of Results; Research paper; ROC Curve; Stomach Neoplasms - diagnostic imaging; Stomach Neoplasms - drug therapy; Stomach Neoplasms - mortality; Tomography, X-Ray Computed - methods; Treatment Outcome
• ISSN: 2352-3964; 2352-3964
• DOI: 10.1016/j.ebiom.2018.09.007

To develop and validate a radiomics signature for the prediction of gastric cancer (GC) survival and chemotherapeutic benefits. In this multicenter retrospective analysis, we analyzed the radiomics features of portal venous-phase computed tomography in 1591 consecutive patients. A radiomics signature was generated by using the Lasso-Cox regression model in 228 patients and validated in internal and external validation cohorts. Radiomics nomograms integrating the radiomics signature were constructed, demonstrating the incremental value of the radiomics signature to the traditional staging system for individualized survival estimation. The performance of the nomograms was assessed with respect to calibration, discrimination, and clinical usefulness. The radiomics signature consisted of 19 selected features and was significantly associated with DFS (disease-free survival) and OS (overall survival). Multivariate analysis demonstrated that the radiomics signature was an independent prognostic factor. Incorporating the radiomics signature into the radiomics-based nomograms resulted in better performance for the estimation of DFS and OS than the clinicopathological nomograms and TNM staging system, with improved accuracy of the classification of survival outcomes. Further analysis showed that stage II and III patients with higher radiomics scores exhibited a favorable response to chemotherapy. In conclusion, the newly developed radiomics signature is a powerful predictor of DFS and OS, and it may predict which patients with stage II and III GC benefit from chemotherapy.