Literature DB >> 27665235

Whole-Lesion Apparent Diffusion Coefficient-Based Entropy-Related Parameters for Characterizing Cervical Cancers: Initial Findings.

Yue Guan1, Weifeng Li1, Zhuoran Jiang1, Ying Chen1, Song Liu2, Jian He3, Zhengyang Zhou4, Yun Ge5.   

Abstract

RATIONALE AND
OBJECTIVES: This study aimed to develop whole-lesion apparent diffusion coefficient (ADC)-based entropy-related parameters of cervical cancer to preliminarily assess intratumoral heterogeneity of this lesion in comparison to adjacent normal cervical tissues.
MATERIALS AND METHODS: A total of 51 women (mean age, 49 years) with cervical cancers confirmed by biopsy underwent 3-T pelvic diffusion-weighted magnetic resonance imaging with b values of 0 and 800 s/mm2 prospectively. ADC-based entropy-related parameters including first-order entropy and second-order entropies were derived from the whole tumor volume as well as adjacent normal cervical tissues. Intraclass correlation coefficient, Wilcoxon test with Bonferroni correction, Kruskal-Wallis test, and receiver operating characteristic curve were used for statistical analysis.
RESULTS: All the parameters showed excellent interobserver agreement (all intraclass correlation coefficients  > 0.900). Entropy, entropy(H)0, entropy(H)45, entropy(H)90, entropy(H)135, and entropy(H)mean were significantly higher, whereas entropy(H)range and entropy(H)std were significantly lower in cervical cancers compared to adjacent normal cervical tissues (all P <.0001). Kruskal-Wallis test showed that there were no significant differences among the values of various second-order entropies including entropy(H)0, entropy(H)45, entropy(H)90, entropy(H)135, and entropy(H)mean. All second-order entropies had larger area under the receiver operating characteristic curve than first-order entropy in differentiating cervical cancers from adjacent normal cervical tissues. Further, entropy(H)45, entropy(H)90, entropy(H)135, and entropy(H)mean had the same largest area under the receiver operating characteristic curve of 0.867.
CONCLUSION: Whole-lesion ADC-based entropy-related parameters of cervical cancers were developed successfully, which showed initial potential in characterizing intratumoral heterogeneity in comparison to adjacent normal cervical tissues.
Copyright © 2016 The Association of University Radiologists. Published by Elsevier Inc. All rights reserved.

Entities:  

Keywords:  Diffusion-weighted magnetic resonance imaging; apparent diffusion coefficient; entropy; texture analysis; uterine cervical neoplasms

Mesh:

Year:  2016        PMID: 27665235     DOI: 10.1016/j.acra.2016.08.010

Source DB:  PubMed          Journal:  Acad Radiol        ISSN: 1076-6332            Impact factor:   3.173


  16 in total

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Authors:  François Lucia; Dimitris Visvikis; Martin Vallières; Marie-Charlotte Desseroit; Omar Miranda; Philippe Robin; Pietro Andrea Bonaffini; Joanne Alfieri; Ingrid Masson; Augustin Mervoyer; Caroline Reinhold; Olivier Pradier; Mathieu Hatt; Ulrike Schick
Journal:  Eur J Nucl Med Mol Imaging       Date:  2018-12-07       Impact factor: 9.236

2.  ADC Histogram Analysis of Cervical Cancer Aids Detecting Lymphatic Metastases-a Preliminary Study.

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3.  Prediction of outcome using pretreatment 18F-FDG PET/CT and MRI radiomics in locally advanced cervical cancer treated with chemoradiotherapy.

Authors:  François Lucia; Dimitris Visvikis; Marie-Charlotte Desseroit; Omar Miranda; Jean-Pierre Malhaire; Philippe Robin; Olivier Pradier; Mathieu Hatt; Ulrike Schick
Journal:  Eur J Nucl Med Mol Imaging       Date:  2017-12-09       Impact factor: 9.236

4.  Histogram analysis of apparent diffusion coefficients for predicting pelvic lymph node metastasis in patients with uterine cervical cancer.

Authors:  Jiyeong Lee; Chan Kyo Kim; Sung Yoon Park
Journal:  MAGMA       Date:  2019-09-23       Impact factor: 2.310

5.  Feasibility of an ADC-based radiomics model for predicting pelvic lymph node metastases in patients with stage IB-IIA cervical squamous cell carcinoma.

Authors:  Yan Yan Yu; Rui Zhang; Rui Tong Dong; Qi Yun Hu; Tao Yu; Fan Liu; Ya Hong Luo; Yue Dong
Journal:  Br J Radiol       Date:  2019-04-01       Impact factor: 3.039

6.  Cervical Cancer: Associations between Metabolic Parameters and Whole Lesion Histogram Analysis Derived from Simultaneous 18F-FDG-PET/MRI.

Authors:  Hans-Jonas Meyer; Sandra Purz; Osama Sabri; Alexey Surov
Journal:  Contrast Media Mol Imaging       Date:  2018-07-30       Impact factor: 3.161

7.  Texture Analysis Using Semiquantitative Kinetic Parameter Maps from DCE-MRI: Preoperative Prediction of HER2 Status in Breast Cancer.

Authors:  Lirong Song; Chunli Li; Jiandong Yin
Journal:  Front Oncol       Date:  2021-06-08       Impact factor: 6.244

8.  Whole-lesion ADC histogram and texture analysis in predicting recurrence of cervical cancer treated with CCRT.

Authors:  Jie Meng; Lijing Zhu; Li Zhu; Li Xie; Huanhuan Wang; Song Liu; Jing Yan; Baorui Liu; Yue Guan; Jian He; Yun Ge; Zhengyang Zhou; Xiaofeng Yang
Journal:  Oncotarget       Date:  2017-09-28

9.  Relationships between histogram analysis of ADC values and complex 18F-FDG-PET parameters in head and neck squamous cell carcinoma.

Authors:  Hans-Jonas Meyer; Sandra Purz; Osama Sabri; Alexey Surov
Journal:  PLoS One       Date:  2018-09-06       Impact factor: 3.240

10.  Texture Analysis of DCE-MRI Intratumoral Subregions to Identify Benign and Malignant Breast Tumors.

Authors:  Bin Zhang; Lirong Song; Jiandong Yin
Journal:  Front Oncol       Date:  2021-07-08       Impact factor: 6.244

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