Performances of Whole Tumor Texture Analysis Based on MRI: Predicting Preoperative T Stage of Rectal Carcinomas

Jia You; Jiandong Yin

doi:10.3389/fonc.2021.678441

Performances of Whole Tumor Texture Analysis Based on MRI: Predicting Preoperative T Stage of Rectal Carcinomas

Front Oncol. 2021 Aug 3:11:678441. doi: 10.3389/fonc.2021.678441. eCollection 2021.

Authors

Jia You¹, Jiandong Yin¹

Affiliation

¹ Department of Radiology, Shengjing Hospital of China Medical University, Shenyang, China.

Abstract

Objective: To determine whether there is a correlation between texture features extracted from high-resolution T2-weighted imaging (HR-T2WI) or apparent diffusion coefficient (ADC) maps and the preoperative T stage (stages T1-2 versus T3-4) in rectal carcinomas.

Materials and methods: One hundred and fifty four patients with rectal carcinomas who underwent preoperative HR-T2WI and diffusion-weighted imaging were enrolled. Patients were divided into training (n = 89) and validation (n = 65) cohorts. 3D Slicer was used to segment the entire volume of interest for whole tumors based on HR-T2WI and ADC maps. The least absolute shrinkage and selection operator (LASSO) was performed to select feature. The significantly difference was tested by the independent sample t-test and Mann-Whitney U test. The support vector machine (SVM) model was used to develop classification models. The correlation between features and T stage was assessed by Spearman's correlation analysis. Multivariate logistic regression analysis was performed to identify independent predictors of tumor invasion. The performance of classifiers was evaluated by the receiver operating characteristic (ROC) curves.

Results: The wavelet HHH NGTDM strength (R_S = -0.364, P < 0.001) from HR-T2WI was an independent predictor of stage T3-4 tumors. The shape maximum 2D diameter column (R_S = 0.431, P < 0.001), log σ = 5.0 mm 3D first-order maximum (R_S = 0.276, P = 0.009), and log σ = 5.0 mm 3D first-order interquartile range (R_S = -0.229, P = 0.032) from ADC maps were independent predictors. In training cohorts, the classification models from HR-T2WI, ADC maps and the combination of two achieved the area under the ROC curves (AUCs) of 0.877, 0.902 and 0.941, with the accuracy of 79.78%, 89.86% and 89.89%, respectively. In validation cohorts, the three models achieved AUCs of 0.845, 0.881 and 0.910, with the accuracy of 78.46%, 83.08% and 87.69%, respectively.

Conclusions: Texture analysis based on ADC maps shows more potential than HR-T2WI in identifying preoperative T stage in rectal carcinomas. The combined application of HR-T2WI and ADC maps may help to improve the accuracy of preoperative diagnosis of rectal cancer invasion.

Keywords: T stage; apparent diffusion coefficient; magnetic resonance imaging; rectal cancer; texture analysis.