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Preoperative T2-weighted MR imaging texture analysis of gastric cancer: prediction of TNM stages

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Abstract

Purpose

To explore the capability of algorithms to build multivariate models integrating morphological and texture features derived from preoperative T2-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.

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Data availability

Data are available upon request to the corresponding author.

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Funding

This study was funded by National Natural Science Foundation of China (grant Number 81601463), Jiangsu Provincial Medical Youth Talent (Grant Number QNRC2016040), and Medical Science and Technology Development Foundation, Nanjing Commission of Health (Grant Number YKK16113). The funding sources had no role in the study design, data collection, data analysis, or interpretation of the findings.

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    Authors and Affiliations

    1. Department of Radiology, Nanjing Drum Tower Hospital Clinical College of Nanjing Medical University, No. 321, Zhongshan Road, Nanjing, 210008, Jiangsu, China

      Xiangmei Qiao, Changfeng Ji, Hui Li, Tingting Shi, Song Liu, Zhengyang Zhou & Kefeng Zhou

    2. State Key Lab of Novel Software Technology, Nanjing University, Nanjing, 210046, China

      Zhengliang Li & Qing Gu

    3. Department of Pathology, Nanjing Drum Tower Hospital Clinical College of Nanjing Medical University, Nanjing, 210008, China

      Lin Li

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    1. Xiangmei Qiao
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    Qiao, X., Li, Z., Li, L. et al. Preoperative T2-weighted MR imaging texture analysis of gastric cancer: prediction of TNM stages. Abdom Radiol 46, 1487–1497 (2021). https://doi.org/10.1007/s00261-020-02802-1

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