Preoperative computed tomography-guided disease-free survival prediction in gastric cancer: a multicenter radiomics study.

PURPOSE

Preoperative and noninvasive prognosis evaluation remains challenging for gastric cancer. Novel preoperative prognostic biomarkers should be investigated. This study aimed to develop multidetector-row computed tomography (MDCT)-guided prognostic models to direct follow-up strategy and improve prognosis.

METHODS

A retrospective dataset of 353 gastric cancer patients were enrolled from two centers and allocated to three cohorts: training cohort (n = 166), internal validation cohort (n = 83), and external validation cohort (n = 104). Quantitative radiomic features were extracted from MDCT images. The least absolute shrinkage and selection operator penalized Cox regression was adopted to construct a radiomic signature. A radiomic nomogram was established by integrating the radiomic signature and significant clinical risk factors. We also built a preoperative tumor-node-metastasis staging model for comparison. All models were evaluated considering the abilities of risk stratification, discrimination, calibration, and clinical use.

RESULTS

In the two validation cohorts, the established four-feature radiomic signature showed robust risk stratification power (P = 0.0260 and 0.0003, log-rank test). The radiomic nomogram incorporated radiomic signature, extramural vessel invasion, clinical T stage, and clinical N stage, outperforming all the other models (concordance index = 0.720 and 0.727) with good calibration and decision benefits. Also, the 2-yr disease-free survival (DFS) prediction was most effective (time-dependent area under curve = 0.771 and 0.765). Moreover, subgroup analysis indicated that the radiomic signature was more sensitive in risk stratifying patients with advanced clinical T/N stage.

CONCLUSIONS

The proposed MDCT-guided radiomic signature was verified as a prognostic factor for gastric cancer. The radiomic nomogram was a noninvasive auxiliary model for preoperative individualized DFS prediction, holding potential in promoting treatment strategy and clinical prognosis.