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

Abstract

ObjectiveTo 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 MethodsOne 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.ResultsThe wavelet HHH NGTDM strength (RS = -0.364, P < 0.001) from HR-T2WI was an independent predictor of stage T3–4 tumors. The shape maximum 2D diameter column (RS = 0.431, P < 0.001), log σ = 5.0 mm 3D first-order maximum (RS = 0.276, P = 0.009), and log σ = 5.0 mm 3D first-order interquartile range (RS = -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.ConclusionsTexture 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.

Radiogenomics study to predict the nuclear grade of renal clear cell carcinoma

“…In addition, glszm_SizeZoneNonUniformity can predict the non-perfusion volume ratio (NPVR) after high-intensity focused ultrasound ablation of uterine fibroids, which could reflect the prognosis after treatment [28] , [29] , [30] . NGTDM_strength was found to be an independent preoperative predictor of T-stage in rectal cancer [31] . The rising popularity of radiogenomics suggests that radiomic features are increasingly relied on to elucidate biological mechanisms related to genes and pathways.…”

Radiomic features derived from pretherapeutic MRI predict chemoradiation response in locally advanced rectal cancer

“…This finding is similar to several other studies, where various wavelet filters and LoG filters were used and effectively generated more discriminative features than features derived from unfiltered images for analyzing rectal cancer MR images. 12,28,30,31…”

MRI‐Based Artificial Intelligence in Rectal Cancer

“…In the validation set, the AUCs of the minimum and maximum delineation models were 0.808 and 0.903, respectively. You et al 15 used the support vector machine (SVM) model to distinguish between T1/T2 and T3/T4 based on HR‐T2WI and apparent diffusion coefficient (ADC) maps of 154 patients. The HR‐T2WI, ADC maps and the combined models achieved AUCs of 0.845, 0.881, and 0.910, with accuracies of 78.46%, 83.08% and 87.69%, respectively, in the validation cohorts.…”