Literature DB >> 27367863

Assessment of histological differentiation in gastric cancers using whole-volume histogram analysis of apparent diffusion coefficient maps.

Yujuan Zhang1, Jun Chen2, Song Liu1, Hua Shi1, Wenxian Guan3, Changfeng Ji4, Tingting Guo5, Huanhuan Zheng1, Yue Guan6, Yun Ge6, Jian He1, Zhengyang Zhou1, Xiaofeng Yang7, Tian Liu7.   

Abstract

PURPOSE: To investigate the efficacy of histogram analysis of the entire tumor volume in apparent diffusion coefficient (ADC) maps for differentiating between histological grades in gastric cancer.
MATERIALS AND METHODS: Seventy-eight patients with gastric cancer were enrolled in a retrospective 3.0T magnetic resonance imaging (MRI) study. ADC maps were obtained at two different b values (0 and 1000 sec/mm2 ) for each patient. Tumors were delineated on each slice of the ADC maps, and a histogram for the entire tumor volume was subsequently generated. A series of histogram parameters (eg, skew and kurtosis) were calculated and correlated with the histological grade of the surgical specimen. The diagnostic performance of each parameter for distinguishing poorly from moderately well-differentiated gastric cancers was assessed by using the area under the receiver operating characteristic curve (AUC).
RESULTS: There were significant differences in the 5th , 10th , 25th , and 50th percentiles, skew, and kurtosis between poorly and well-differentiated gastric cancers (P < 0.05). There were correlations between the degrees of differentiation and histogram parameters, including the 10th percentile, skew, kurtosis, and max frequency; the correlation coefficients were 0.273, -0.361, -0.339, and -0.370, respectively. Among all the histogram parameters, the max frequency had the largest AUC value, which was 0.675.
CONCLUSION: Histogram analysis of the ADC maps on the basis of the entire tumor volume can be useful in differentiating between histological grades for gastric cancer. LEVEL OF EVIDENCE: 4 J. Magn. Reson. Imaging 2017;45:440-449.
© 2016 International Society for Magnetic Resonance in Medicine.

Entities:  

Keywords:  cell differentiation; diffusion magnetic resonance imaging; histogram; magnetic resonance imaging; stomach neoplasm

Mesh:

Year:  2016        PMID: 27367863     DOI: 10.1002/jmri.25360

Source DB:  PubMed          Journal:  J Magn Reson Imaging        ISSN: 1053-1807            Impact factor:   4.813


  20 in total

1.  CT radiomics nomogram for the preoperative prediction of lymph node metastasis in gastric cancer.

Authors:  Yue Wang; Wei Liu; Yang Yu; Jing-Juan Liu; Hua-Dan Xue; Ya-Fei Qi; Jing Lei; Jian-Chun Yu; Zheng-Yu Jin
Journal:  Eur Radiol       Date:  2019-08-29       Impact factor: 5.315

Review 2.  Imaging strategies in the management of gastric cancer: current role and future potential of MRI.

Authors:  Alicia S Borggreve; Lucas Goense; Hylke J F Brenkman; Stella Mook; Gert J Meijer; Frank J Wessels; Marcel Verheij; Edwin P M Jansen; Richard van Hillegersberg; Peter S N van Rossum; Jelle P Ruurda
Journal:  Br J Radiol       Date:  2019-03-05       Impact factor: 3.039

3.  Pre-TACE kurtosis of ADCtotal derived from histogram analysis for diffusion-weighted imaging is the best independent predictor of prognosis in hepatocellular carcinoma.

Authors:  Li-Fang Wu; Sheng-Xiang Rao; Peng-Ju Xu; Li Yang; Cai-Zhong Chen; Hao Liu; Jian-Feng Huang; Cai-Xia Fu; Alice Halim; Meng-Su Zeng
Journal:  Eur Radiol       Date:  2018-06-19       Impact factor: 5.315

4.  Volumetric Histogram Analysis of Apparent Diffusion Coefficient as a Biomarker to Predict Survival of Esophageal Cancer Patients.

Authors:  Atsushi Hirata; Koichi Hayano; Gaku Ohira; Shunsuke Imanishi; Toshiharu Hanaoka; Takeshi Toyozumi; Kentaro Murakami; Tomoyoshi Aoyagi; Kiyohiko Shuto; Hisahiro Matsubara
Journal:  Ann Surg Oncol       Date:  2020-02-25       Impact factor: 5.344

5.  Application of CT texture analysis in predicting histopathological characteristics of gastric cancers.

Authors:  Shunli Liu; Song Liu; Changfeng Ji; Huanhuan Zheng; Xia Pan; Yujuan Zhang; Wenxian Guan; Ling Chen; Yue Guan; Weifeng Li; Jian He; Yun Ge; Zhengyang Zhou
Journal:  Eur Radiol       Date:  2017-06-22       Impact factor: 5.315

6.  Volumetric apparent diffusion coefficient histogram analysis of the testes in nonobstructive azoospermia: a noninvasive fingerprint of impaired spermatogenesis?

Authors:  Athina C Tsili; Loukas G Astrakas; Anna C Goussia; Nikolaos Sofikitis; Maria I Argyropoulou
Journal:  Eur Radiol       Date:  2022-04-29       Impact factor: 5.315

7.  Differentiation of brain metastases originating from lung and breast cancers using apparent diffusion coefficient histogram analysis and the relation of histogram parameters with Ki-67.

Authors:  Mustafa Bozdağ; Ali Er; Sümeyye Ekmekçi
Journal:  Neuroradiol J       Date:  2021-10-05

Review 8.  The role of MRI in the diagnosis and treatment of gastric cancer.

Authors:  Yingjing Zhang; Jianchun Yu
Journal:  Diagn Interv Radiol       Date:  2020-05       Impact factor: 2.630

9.  Histological grades of rectal cancer: whole-volume histogram analysis of apparent diffusion coefficient based on reduced field-of-view diffusion-weighted imaging.

Authors:  Yang Peng; Hao Tang; Xiaoyan Meng; Yaqi Shen; Daoyu Hu; Ihab Kamel; Zhen Li
Journal:  Quant Imaging Med Surg       Date:  2020-01

10.  A Machine Learning Model for Predicting a Major Response to Neoadjuvant Chemotherapy in Advanced Gastric Cancer.

Authors:  Yonghe Chen; Kaikai Wei; Dan Liu; Jun Xiang; Gang Wang; Xiaochun Meng; Junsheng Peng
Journal:  Front Oncol       Date:  2021-06-01       Impact factor: 6.244

View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.