Literature DB >> 29063250

Whole-tumour diffusion kurtosis MR imaging histogram analysis of rectal adenocarcinoma: Correlation with clinical pathologic prognostic factors.

Yanfen Cui1, Xiaotang Yang2, Xiaosong Du1, Zhizheng Zhuo3, Lei Xin1, Xintao Cheng1.   

Abstract

OBJECTIVE: To investigate potential relationships between diffusion kurtosis imaging (DKI)-derived parameters using whole-tumour volume histogram analysis and clinicopathological prognostic factors in patients with rectal adenocarcinoma.
MATERIAL AND METHODS: 79 consecutive patients who underwent MRI examination with rectal adenocarcinoma were retrospectively evaluated. Parameters D, K and conventional ADC were measured using whole-tumour volume histogram analysis. Student's t-test or Mann-Whitney U-test, receiver operating characteristic curves and Spearman's correlation were used for statistical analysis.
RESULTS: Almost all the percentile metrics of K were correlated positively with nodal involvement, higher histological grades, the presence of lymphangiovascular invasion (LVI) and circumferential margin (CRM) (p<0.05), with the exception of between K10th, K90th and histological grades. In contrast, significant negative correlations were observed between 25th, 50th percentiles and mean values of ADC and D, as well as ADC10th, with tumour T stages (p< 0.05). Meanwhile, lower 75th and 90th percentiles of ADC and D values were also correlated inversely with nodal involvement (p< 0.05). Kmean showed a relatively higher area under the curve (AUC) and higher specificity than other percentiles for differentiation of lesions with nodal involvement.
CONCLUSION: DKI metrics with whole-tumour volume histogram analysis, especially K parameters, were associated with important prognostic factors of rectal cancer. KEY POINTS: • K correlated positively with some important prognostic factors of rectal cancer. • K mean showed higher AUC and specificity for differentiation of nodal involvement. • DKI metrics with whole-tumour volume histogram analysis depicted tumour heterogeneity.

Entities:  

Keywords:  Biomarker; Diffusion kurtosis imaging; Diffusion-weighted imaging; Prognostic factors; Rectal cancer

Mesh:

Year:  2017        PMID: 29063250     DOI: 10.1007/s00330-017-5094-3

Source DB:  PubMed          Journal:  Eur Radiol        ISSN: 0938-7994            Impact factor:   5.315


  34 in total

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2.  Diagnostic accuracy of MRI for assessment of T category, lymph node metastases, and circumferential resection margin involvement in patients with rectal cancer: a systematic review and meta-analysis.

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3.  Perineural invasion in pT3N0 rectal cancer: the incidence and its prognostic effect.

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Journal:  Cancer       Date:  2010-11-08       Impact factor: 6.860

4.  Breast Cancer: Diffusion Kurtosis MR Imaging-Diagnostic Accuracy and Correlation with Clinical-Pathologic Factors.

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Authors:  Michiaki Akashi; Yuji Nakahusa; Tomomi Yakabe; Yoshiyuki Egashira; Yasuo Koga; Kenji Sumi; Hirokazu Noshiro; Hiroyuki Irie; Osamu Tokunaga; Kohji Miyazaki
Journal:  Acta Radiol       Date:  2013-09-04       Impact factor: 1.990

6.  Diffusional kurtosis imaging: the quantification of non-gaussian water diffusion by means of magnetic resonance imaging.

Authors:  Jens H Jensen; Joseph A Helpern; Anita Ramani; Hanzhang Lu; Kyle Kaczynski
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10.  Apparent Diffusion Coefficient (ADC) value: a potential imaging biomarker that reflects the biological features of rectal cancer.

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Journal:  PLoS One       Date:  2014-10-10       Impact factor: 3.240

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  16 in total

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

Authors:  Zhihua Lu; Lei Wang; Kaijian Xia; Heng Jiang; Xiaoyan Weng; Jianlong Jiang; Mei Wu
Journal:  J Med Syst       Date:  2019-11-07       Impact factor: 4.460

Review 2.  Diffusion-weighted imaging in rectal cancer: current applications and future perspectives.

Authors:  Niels W Schurink; Doenja M J Lambregts; Regina G H Beets-Tan
Journal:  Br J Radiol       Date:  2019-03-05       Impact factor: 3.039

Review 3.  Multiparametric MRI in rectal cancer.

Authors:  Bengi Gürses; Medine Böge; Emre Altınmakas; Emre Balık
Journal:  Diagn Interv Radiol       Date:  2019-05       Impact factor: 2.630

4.  Predicting perineural invasion using histogram analysis of zoomed EPI diffusion-weighted imaging in rectal cancer.

Authors:  Lijuan Wan; Wenjing Peng; Shuangmei Zou; Qinglei Shi; Peihua Wu; Qing Zhao; Feng Ye; Xinming Zhao; Hongmei Zhang
Journal:  Abdom Radiol (NY)       Date:  2022-07-02

5.  Diffusion kurtosis imaging (DKI) of hepatocellular carcinoma: correlation with microvascular invasion and histologic grade.

Authors:  Likun Cao; Jie Chen; Ting Duan; Min Wang; Hanyu Jiang; Yi Wei; Chunchao Xia; Xiaoyue Zhou; Xu Yan; Bin Song
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6.  Renal cell carcinoma: preoperative evaluate the grade of histological malignancy using volumetric histogram analysis derived from magnetic resonance diffusion kurtosis imaging.

Authors:  Ke Wang; Jingyun Cheng; Yan Wang; Guangyao Wu
Journal:  Quant Imaging Med Surg       Date:  2019-04

7.  Preoperative volumetric synthetic magnetic resonance imaging of the primary tumor for a more accurate prediction of lymph node metastasis in rectal cancer.

Authors:  Li Zhao; Meng Liang; Zhuo Shi; Lizhi Xie; Hongmei Zhang; Xinming Zhao
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8.  T2*-weighted imaging and diffusion kurtosis imaging (DKI) of rectal cancer: correlation with clinical histopathologic prognostic factors.

Authors:  Shan Hu; Yang Peng; Qiushi Wang; Bin Liu; Ihab Kamel; Zaiyi Liu; Changhong Liang
Journal:  Abdom Radiol (NY)       Date:  2021-12-27

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.  Preoperatively Grading Rectal Cancer with the Combination of Intravoxel Incoherent Motions Imaging and Diffusion Kurtosis Imaging.

Authors:  Zhijun Geng; Yunfei Zhang; Shaohan Yin; Shanshan Lian; Haoqiang He; Hui Li; Chuanmiao Xie; Yongming Dai
Journal:  Contrast Media Mol Imaging       Date:  2020-10-12       Impact factor: 3.161

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