Preoperative T 2 -weighted MR imaging texture analysis of gastric cancer: prediction of TNM stages
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Preoperative T2‑weighted MR imaging texture analysis of gastric cancer: prediction of TNM stages Xiangmei Qiao1 · Zhengliang Li2 · Lin Li3 · Changfeng Ji1 · Hui Li1 · Tingting Shi1 · Qing Gu2 · Song Liu1 · Zhengyang Zhou1 · Kefeng Zhou1 Received: 30 June 2020 / Revised: 20 September 2020 / Accepted: 29 September 2020 © Springer Science+Business Media, LLC, part of Springer Nature 2020
Abstract Purpose To explore the capability of algorithms to build multivariate models integrating morphological and texture features derived from preoperative T 2-weighted magnetic resonance (MR) images of gastric cancer (GC) to evaluate tumor- (T), node- (N), and metastasis- (M) stages. Methods A total of 80 patients at our hospital who underwent abdominal MR imaging and were diagnosed with GC from December 2011 to November 2016 were retrospectively included. Texture features were calculated using T2-weighted images with a manual region of interest. Morphological characteristics were also evaluated. Classifiers and regression analyses were used to build multivariate models. Receiver operating characteristic (ROC) curve analysis was performed to assess diagnostic efficacy. Results There were 8, 10, and 3 texture parameters that showed significant differences in GCs at different overall (I–II vs. III–IV), T (1–2 vs. 3–4), and N (− vs. +) stages (all p < 0.05), respectively. Mild thickening was more common in stages I–II, T1–2, and N− GCs (all p < 0.05). An irregular outer contour was more commonly observed in stages III–IV (p = 0.001) and T3–4 (p = 0.001) GCs. T3–4 and N+ GCs tended to be thickening type lesions (p = 0.005 and 0.032, respectively). The multivariate models using the naive bayes algorithm showed the highest diagnostic efficacy in predicting T and N stages (area under the ROC curves [AUC] = 0.900 and 0.863, respectively), and the model based on regression analysis had the best predictive performance in overall staging (AUC = 0.839). Conclusion Multivariate models combining morphological characteristics with texture parameters based on machine learning algorithms were able to improve diagnostic efficacy in predicting the overall, T, and N stages of GCs. Keywords Gastric cancer · Magnetic resonance imaging · Staging · TNM · Texture analysis
Introduction Xiangmei Qiao and Zhengliang Li contributed equally to this manuscript. Electronic supplementary material The online version of this article (https://doi.org/10.1007/s00261-020-02802-1) contains supplementary material, which is available to authorized users. * Kefeng Zhou [email protected] 1
Department of Radiology, Nanjing Drum Tower Hospital Clinical College of Nanjing Medical University, No. 321, Zhongshan Road, Nanjing 210008, Jiangsu, China
2
State Key Lab of Novel Software Technology, Nanjing University, Nanjing 210046, China
3
Department of Pathology, Nanjing Drum Tower Hospital Clinical College of Nanjing Medical University, Nanjing 210008, China
Gastric cancer (GC) is one of the most common malignant tumors of the
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