Prognostic Value of Texture Analysis Based on Pretreatment DWI-Weighted MRI for Esophageal Squamous Cell Carcinoma Patients Treated With Concurrent Chemo-Radiotherapy

Purpose: The purpose of the research was to assess the prognostic value of three-dimensional (3D) texture features based on diffusion-weighted magnetic resonance imaging (DWI) for esophageal squamous cell carcinoma (ESCC) patients undergoing concurrent chemo-radiotherapy (CRT).Methods: We prospectiv...

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Main Authors: Zhenjiang Li, Chun Han, Lan Wang, Jian Zhu, Yong Yin, Baosheng Li
Format: Article
Language:English
Published: Frontiers Media S.A. 2019-10-01
Series:Frontiers in Oncology
Subjects:
Online Access:https://www.frontiersin.org/article/10.3389/fonc.2019.01057/full
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author Zhenjiang Li
Chun Han
Lan Wang
Jian Zhu
Yong Yin
Baosheng Li
author_facet Zhenjiang Li
Chun Han
Lan Wang
Jian Zhu
Yong Yin
Baosheng Li
author_sort Zhenjiang Li
collection DOAJ
description Purpose: The purpose of the research was to assess the prognostic value of three-dimensional (3D) texture features based on diffusion-weighted magnetic resonance imaging (DWI) for esophageal squamous cell carcinoma (ESCC) patients undergoing concurrent chemo-radiotherapy (CRT).Methods: We prospectively enrolled 82 patients with ESCC into a cohort study. Two DWI sequences (b = 0 and b = 600 s/mm2) were acquired along with axial T2WI and T1WI before CRT. Two groups of features were examined: (1) clinical and demographic features (e.g., TNM stage, age and sex) and (2) changes in spatial texture characteristics of the apparent diffusion coefficient (ADC), which characterizes gray intensity changes in tumor areas, spatial pattern and distribution, and related changes caused by CRT. Reproducible feature sets without redundancy were statistically filtered and validated. The prognostic values associated with overall survival (OS) for each parameter were studied using Kaplan-Meier and Cox regression models for univariate and multivariate analyses, respectively.Results: Both univariate and multivariate Cox model analyses showed that the energy of intensity histogram texture (IHIST_energy), radiation dose, mean of the contrast in distance 1 of 26 directions (m_contrast_1), extreme difference of the homogeneity in distance 2 of 26 directions (Diff_homogeneity_2), mean of the inverse variance in distance 2 of 26 directions (m_lnversevariance_2), high-intensity small zone emphasis (HISE), and low-intensity large zone emphasis (LILE) were significantly associated with survival. The results showed that 6 texture parameters extracted from the ADC images before treatment could distinguish among high-, medium-, and low-risk groups (log-rank χ2 = 9.7; P = 0.00773). The biased C-index value was 0.715 (95% CI: 0.708 to 0.732) based on bootstrapping validation.Conclusions: The ADC 3D texture feature can be used as a useful biomarker to predict the survival of ESCC patients undergoing CRT. Combining ADC 3D texture features with conventional prognostic factors can generate reliable survival prediction models.
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spelling doaj.art-3b6e29e69f1d48b5b6ea3b86182fd4462022-12-21T18:33:56ZengFrontiers Media S.A.Frontiers in Oncology2234-943X2019-10-01910.3389/fonc.2019.01057456198Prognostic Value of Texture Analysis Based on Pretreatment DWI-Weighted MRI for Esophageal Squamous Cell Carcinoma Patients Treated With Concurrent Chemo-RadiotherapyZhenjiang Li0Chun Han1Lan Wang2Jian Zhu3Yong Yin4Baosheng Li5Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Shandong Cancer Hospital, Jinan, ChinaFourth Hospital of Hebei Medical University, Shijiazhuang, ChinaFourth Hospital of Hebei Medical University, Shijiazhuang, ChinaShandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Shandong Cancer Hospital, Jinan, ChinaShandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Shandong Cancer Hospital, Jinan, ChinaShandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Shandong Cancer Hospital, Jinan, ChinaPurpose: The purpose of the research was to assess the prognostic value of three-dimensional (3D) texture features based on diffusion-weighted magnetic resonance imaging (DWI) for esophageal squamous cell carcinoma (ESCC) patients undergoing concurrent chemo-radiotherapy (CRT).Methods: We prospectively enrolled 82 patients with ESCC into a cohort study. Two DWI sequences (b = 0 and b = 600 s/mm2) were acquired along with axial T2WI and T1WI before CRT. Two groups of features were examined: (1) clinical and demographic features (e.g., TNM stage, age and sex) and (2) changes in spatial texture characteristics of the apparent diffusion coefficient (ADC), which characterizes gray intensity changes in tumor areas, spatial pattern and distribution, and related changes caused by CRT. Reproducible feature sets without redundancy were statistically filtered and validated. The prognostic values associated with overall survival (OS) for each parameter were studied using Kaplan-Meier and Cox regression models for univariate and multivariate analyses, respectively.Results: Both univariate and multivariate Cox model analyses showed that the energy of intensity histogram texture (IHIST_energy), radiation dose, mean of the contrast in distance 1 of 26 directions (m_contrast_1), extreme difference of the homogeneity in distance 2 of 26 directions (Diff_homogeneity_2), mean of the inverse variance in distance 2 of 26 directions (m_lnversevariance_2), high-intensity small zone emphasis (HISE), and low-intensity large zone emphasis (LILE) were significantly associated with survival. The results showed that 6 texture parameters extracted from the ADC images before treatment could distinguish among high-, medium-, and low-risk groups (log-rank χ2 = 9.7; P = 0.00773). The biased C-index value was 0.715 (95% CI: 0.708 to 0.732) based on bootstrapping validation.Conclusions: The ADC 3D texture feature can be used as a useful biomarker to predict the survival of ESCC patients undergoing CRT. Combining ADC 3D texture features with conventional prognostic factors can generate reliable survival prediction models.https://www.frontiersin.org/article/10.3389/fonc.2019.01057/fullesophageal squamous cell cancertexture analysismagnetic resonance imagingdiffusion-weighted magnetic resonance imagingchemo-radiotherapy
spellingShingle Zhenjiang Li
Chun Han
Lan Wang
Jian Zhu
Yong Yin
Baosheng Li
Prognostic Value of Texture Analysis Based on Pretreatment DWI-Weighted MRI for Esophageal Squamous Cell Carcinoma Patients Treated With Concurrent Chemo-Radiotherapy
Frontiers in Oncology
esophageal squamous cell cancer
texture analysis
magnetic resonance imaging
diffusion-weighted magnetic resonance imaging
chemo-radiotherapy
title Prognostic Value of Texture Analysis Based on Pretreatment DWI-Weighted MRI for Esophageal Squamous Cell Carcinoma Patients Treated With Concurrent Chemo-Radiotherapy
title_full Prognostic Value of Texture Analysis Based on Pretreatment DWI-Weighted MRI for Esophageal Squamous Cell Carcinoma Patients Treated With Concurrent Chemo-Radiotherapy
title_fullStr Prognostic Value of Texture Analysis Based on Pretreatment DWI-Weighted MRI for Esophageal Squamous Cell Carcinoma Patients Treated With Concurrent Chemo-Radiotherapy
title_full_unstemmed Prognostic Value of Texture Analysis Based on Pretreatment DWI-Weighted MRI for Esophageal Squamous Cell Carcinoma Patients Treated With Concurrent Chemo-Radiotherapy
title_short Prognostic Value of Texture Analysis Based on Pretreatment DWI-Weighted MRI for Esophageal Squamous Cell Carcinoma Patients Treated With Concurrent Chemo-Radiotherapy
title_sort prognostic value of texture analysis based on pretreatment dwi weighted mri for esophageal squamous cell carcinoma patients treated with concurrent chemo radiotherapy
topic esophageal squamous cell cancer
texture analysis
magnetic resonance imaging
diffusion-weighted magnetic resonance imaging
chemo-radiotherapy
url https://www.frontiersin.org/article/10.3389/fonc.2019.01057/full
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