Retrospective Study
Copyright ©The Author(s) 2020. Published by Baishideng Publishing Group Inc. All rights reserved.
World J Gastroenterol. May 7, 2020; 26(17): 2082-2096
Published online May 7, 2020. doi: 10.3748/wjg.v26.i17.2082
Prediction of different stages of rectal cancer: Texture analysis based on diffusion-weighted images and apparent diffusion coefficient maps
Jian-Dong Yin, Li-Rong Song, He-Cheng Lu, Xu Zheng
Jian-Dong Yin, Li-Rong Song, Department of Radiology, Shengjing Hospital of China Medical University, Shenyang 110003, Liaoning Province, China
He-Cheng Lu, College of Medicine and Biological Information Engineering, Northeastern University, Shenyang 110036, Liaoning Province, China
Xu Zheng, Department of Clinical Oncology, Shengjing Hospital of China Medical University, Shenyang 110011, Liaoning Province, China
Author contributions: Yin JD designed this study; Lu HC performed the research; Song LR wrote the paper; Yin JD supervised the report; Zheng X provided clinical advice.
Supported by Research and Development Foundation for Major Science and Technology from Shenyang, No. 19-112-4-105; Big Data Foundation for Health Care from China Medical University, No. HMB201902105; and Natural Fund Guidance Plan from Liaoning, No. 2019-ZD-0743.
Institutional review board statement: This study was reviewed and approved by the Ethics Committee of Shengjing Hospital of China Medical University.
Informed consent statement: Written informed consent was acquired from each patient.
Conflict-of-interest statement: All authors declare no conflicts-of-interest related to this article.
Data sharing statement: No additional data are available.
Open-Access: This article is an open-access article that was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution NonCommercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/
Corresponding author: Xu Zheng, MD, Associate Professor, Department of Clinical Oncology, Shengjing Hospital of China Medical University, No. 39, Huaxiang Street, Tiexi District, Shenyang 110011, Liaoning Province, China. cmuzhengxu@126.com
Received: February 6, 2020
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
Abstract
BACKGROUND

It is evident that an accurate evaluation of T and N stage rectal cancer is essential for treatment planning. It has not been extensively investigated whether texture features derived from diffusion-weighted imaging (DWI) images and apparent diffusion coefficient (ADC) maps are associated with the extent of local invasion (pathological stage T1-2 vs T3-4) and nodal involvement (pathological stage N0 vs N1-2) in rectal cancer.

AIM

To predict different stages of rectal cancer using texture analysis based on DWI images and ADC maps.

METHODS

One hundred and fifteen patients with pathologically proven rectal cancer, who underwent preoperative magnetic resonance imaging, including DWI, were enrolled, retrospectively. The ADC measurements (ADCmean, ADCmin, ADCmax) as well as texture features, including the gray level co-occurrence matrix parameters, the gray level run-length matrix parameters and wavelet parameters were calculated based on DWI (b = 0 and b = 1000) images and the ADC maps. Independent sample t-tests or Mann-Whitney U tests were used for statistical analysis. Multivariate logistic regression analysis was conducted to establish the models. The predictive performance was validated by receiver operating characteristic curve analysis.

 

RESULTS

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 area under the operating characteristic curve 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 (SymletH) from DWIb=0 images, sum average, information correlation, long run low gray level emphasis and SymletH from DWIb=1000 images, and ADCmax, ADCmean and information correlation from ADC maps were identified as independent predictors of nodal involvement. The area under the operating characteristic curve of the model reached 0.802 with a sensitivity of 80.77% and a specificity of 68.25%.

CONCLUSION

Texture features extracted from DWI images and ADC maps are useful clues for predicting pathological T and N stages in rectal cancer.

Keywords: Rectal cancer; Diffusion weighted imaging; Apparent diffusion coefficient; Texture analysis

Core tip: This retrospective study investigated the correlations between stages of rectal cancer and texture features from diffusion-weighted images and apparent diffusion coefficient maps. The area under the operating characteristic curve reached 0.793 for identifying local invasion (T stage), and reached 0.802 for determining nodal involvement (N stage). Texture analysis based on diffusion-weighted images and apparent diffusion coefficient maps showed potential value in classifying N and T stage rectal cancer.