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Preoperative computed tomography‐guided disease‐free survival prediction in gastric cancer: a multicenter radiomics study
Author(s) -
Wang Siwen,
Feng Caizhen,
Dong Di,
Li Hailin,
Zhou Jing,
Ye Yingjiang,
Liu Zaiyi,
Tian Jie,
Wang Yi
Publication year - 2020
Publication title -
medical physics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.473
H-Index - 180
eISSN - 2473-4209
pISSN - 0094-2405
DOI - 10.1002/mp.14350
Subject(s) - nomogram , medicine , stage (stratigraphy) , proportional hazards model , concordance , radiomics , radiology , retrospective cohort study , t stage , cohort , imaging biomarker , cancer , oncology , magnetic resonance imaging , paleontology , biology
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.