Published online May 7, 2020. doi: 10.3748/wjg.v26.i17.2082
Peer-review started: February 6, 2020
First decision: February 29, 2020
Revised: March 26, 2020
Accepted: April 15, 2020
Article in press: April 15, 2020
Published online: May 7, 2020
Processing time: 91 Days and 3.1 Hours
Colorectal cancer is the third leading cause of cancer worldwide, and rectal cancer accounts for approximately 30%-35% of colorectal cancer cases. An accurate evaluation of T and N stage in rectal cancer is essential for treatment planning. Heterogeneity within the tumor is a powerful predictor of pathological stage, which can be captured by diffusion-weighted imaging (DWI) images and apparent diffusion coefficient (ADC) maps with texture analysis.
A search of PubMed database indicates that texture analysis of DWI images combined with ADC maps for preoperative staging of rectal cancer has not been reported.
The aim of this study was to investigate whether texture features derived from DWI images and ADC maps were associated with the pathological stage T1-2 vs T3-4 and pathological stage N0 vs N1-2 in rectal cancer.
One hundred and fifteen eligible patients were selected for analyses. Lesion segmentation was performed manually. Twelve texture features were calculated from DWI images and ADC maps, including the gray level co-occurrence matrix parameters, the gray level run-length matrix parameters and wavelet parameters. Moreover, ADC values were measured from the lesion area. An independent sample t-test or Mann-Whitney U test was used to compare parameters between T1-2 and T3-4 stages, and between N0 and N1-2 stages. Multivariate logistic regression analysis was performed with the entry of variables to identify independent factors for T3-4 and N1-2 tumors. Receiver operating characteristic analysis was conducted to evaluate the diagnostic performance of the established logistic models for prediction of T3-4 and N1-2 tumors by calculating the area under the receiver operating curve (AUC).
Dissimilarity, sum average, information correlation and run-length nonuniformity from DWIb=0 images, gray level nonuniformity, run percentage and run-length nonuniformity from DWIb=1000 images, and dissimilarity and run percentage from ADC maps were found to be independent predictors of local invasion (stage T3-4). The AUC of the model reached 0.793 with a sensitivity of 78.57% and a specificity of 74.19%. Sum average, gray level nonuniformity and the horizontal components of symlet transform from DWIb=0 images, sum average, information correlation, long run low gray level emphasis and horizontal components of symlet transform from DWIb=1000 images, and ADCmax, ADCmean and information correlation from ADC maps were identified as independent predictors of nodal involvement. The AUC of the model reached 0.802 with a sensitivity of 80.77% and a specificity of 68.25%.
The results indicated that texture features derived from preoperative DWI images combined with ADC maps were significantly associated with T and N stage in rectal cancer. These findings may be of value for the selection of treatment strategies.
In this project, we evaluated the role of ADC maps and DWI images in determining pathological stage, and the results revealed that texture features could be considered as novel biomarkers to predict the N and T stage in rectal cancer. In a subsequent study, a randomized multi-center prospective trial could be conducted to further validate our findings.