Evaluation of Lymph Node Metastasis in Advanced Gastric Cancer Using Magnetic Resonance Imaging-Based Radiomics - Chen, Wujie; Wang, Siwen; Dong, Di; Gao, Xuning; Zhou, Kefeng; Li, Jiaying; Lv, Bin; Li, Hailin; Wu, Xiangjun; Fang, Mengjie; Tian, Jie; Xu, Maosheng (Frontiers in Oncology, 2019)
Objective: To develop and evaluate a diffusion-weighted imaging (DWI)-based radiomic nomogram for lymph node metastasis (LNM) prediction in advanced gastric cancer (AGC) patients. Overall Study: This retrospective study was conducted with 146 consecutively included pathologically confirmed AGC patients from two centers. All patients underwent preoperative 3.0T magnetic resonance imaging (MRI) examination. The dataset was allocated to a training cohort (n = 71) and an internal validation cohort (n = 47) from one center along with an external validation cohort (n = 28) from another. A summary of 1305 radiomic features were extracted per patient. The least absolute shrinkage and selection operator logistic regression and learning vector quantization methods with cross-validations were adopted to select significant features in a radiomic signature. Combining the radiomic signature and independent clinical factors, a radiomic nomogram was established. The MRI-reported N staging and the MRI-derived model were built for comparison. Model performance was evaluated considering receiver operating characteristic (ROC) analysis, calibration curves, and decision curve analysis. Results: A two-feature radiomic signature was found significantly associated with LNM (p <0.01, training and internal validation cohorts). A radiomic nomogram was established by incorporating the clinical minimum apparent diffusion coefficient (ADC) and MRI-reported N staging. The radiomic nomogram showed a favorable classification ability with an area under ROC curve (AUC) of 0.850 (95% confidence interval [CI], 0.758 – 0.942) in the training cohort, which was then confirmed with an AUC of 0.857 (95% CI, 0.714 – 1.000) in internal validation cohort and 0.878 (95%CI, 0.696 – 1.000) in external validation cohort. Meanwhile, the specificity, sensitivity, and accuracy were 0.846, 0.853, 0.851 in internal validation cohort, and 0.714, 0.952, 0.893 in external validation cohort, compensating for the MRI-reported N staging and MRI-derived model. Decision curve analysis demonstrated good clinical use of radiomic nomogram. Conclusions: This study put forward a DWI-based radiomic nomogram incorporating the radiomic signature, minimum ADC, and MRI-reported N staging for individualized preoperative detection of LNM in patients with AGC.