Prediction of Clinical Pathologic Prognostic Factors for Rectal Adenocarcinoma: Volumetric Texture Analysis Based on Apparent Diffusion Coefficient Maps

Texture analysis has been used to characterize and measure tissue heterogeneity in medical images. The purpose of this study was to investigate the potential of texture features derived from apparent diffusion coefficient (ADC) maps, to serve as imaging markers for predicting important histopatholog...

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Bibliographic Details
Published in:Journal of medical systems Vol. 43; no. 12; p. 331
Main Authors: Lu, Zhihua, Wang, Lei, Xia, Kaijian, Jiang, Heng, Weng, Xiaoyan, Jiang, Jianlong, Wu, Mei
Format: Journal Article
Language:English
Published: New York Springer US 01.12.2019
Springer Nature B.V
Subjects:
Adenocarcinoma
Adenocarcinoma - diagnostic imaging
Adenocarcinoma - pathology
Adult
Aged
Aged, 80 and over
Algorithms
Cancer
Colorectal cancer
Diffusion
Diffusion coefficient
Diffusion Magnetic Resonance Imaging - methods
Distributed Analytics and Deep Learning in Health Care
Energy
Entropy
Female
Health Informatics
Health Sciences
Heterogeneity
Histograms
Humans
Image & Signal Processing
Image Interpretation, Computer-Assisted - methods
Kurtosis
Logistic Models
Magnetic resonance imaging
Male
Medical imaging
Medical prognosis
Medicine
Medicine & Public Health
Middle Aged
Neoplasm Grading
Predictions
Prognosis
Prospective Studies
Rectal Neoplasms - diagnostic imaging
Rectal Neoplasms - pathology
Rectum
Regression analysis
ROC Curve
Skewness
Statistical analysis
Statistics for Life Sciences
Texture
Tumors
Diffusion-weighted imaging
Texture analysis
Rectal cancer
Apparent diffusion coefficient
ISSN:0148-5598, 1573-689X, 1573-689X
Online Access:Get full text
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Summary:Texture analysis has been used to characterize and measure tissue heterogeneity in medical images. The purpose of this study was to investigate the potential of texture features derived from apparent diffusion coefficient (ADC) maps, to serve as imaging markers for predicting important histopathologic prognostic factors in rectal cancer. One hundred patients of rectal cancer received 3 T preoperative magnetic resonance imaging including diffusion-weighted imaging (DWI). Skewness, kurtosis, uniformity from the histogram and entropy, energy, inertia, correlation from gray-level co-occurrence matrix (GLCM) derived from whole-lesion volumes were measured. Independent sample t -test or Mann-Whitney U -test and receiver operating characteristic (ROC) curves were used for statistical analysis. Uniformity, energy and entropy were significantly different ( p  = 0.026, 0.001, and 0.006, respectively) between stage pT1–2 and pT3–4 tumors. Skewness, kurtosis and correlation were significantly different ( p  = 0.000, 0.006, and 0.041, respectively) between grade 1–2 and grade 3 tumors. Energy and entropy ( p  = 0.008 and 0.033, respectively) could significantly differentiate negative circumferential resection margin (CRM) from positive CRM. Furthermore, predicted probabilities derived by logistic regression analysis yielded greater area under the curve (AUC) in differentiating pT3–4 stage and grade 3 grade tumors. Texture features derived from ADC maps may useful to predict important histopathologic prognostic factors of rectal cancer.
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ISSN:0148-5598
1573-689X
1573-689X
DOI:10.1007/s10916-019-1464-5