Literature DB >> 29383521

Standard diffusion-weighted, diffusion kurtosis and intravoxel incoherent motion MR imaging of sinonasal malignancies: correlations with Ki-67 proliferation status.

Zebin Xiao1, Yufeng Zhong1,2, Zuohua Tang3, Jinwei Qiang4, Wen Qian1, Rong Wang1, Jie Wang5, Lingjie Wu6, Wenlin Tang7, Zhongshuai Zhang7.   

Abstract

OBJECTIVES: To explore the correlations of parameters derived from standard diffusion-weighted imaging (DWI), diffusion kurtosis imaging (DKI) and intravoxel incoherent motion (IVIM) with the Ki-67 proliferation status.
METHODS: Seventy-five patients with histologically proven sinonasal malignancies who underwent standard DWI, DKI and IVIM were retrospectively reviewed. The mean, minimum, maximum and whole standard DWI [apparent diffusion coefficient (ADC)], DKI [diffusion kurtosis (K) and diffusion coefficient (Dk)] and IVIM [pure diffusion coefficient (D), pseudo-diffusion coefficient (D*) and perfusion fraction (f)] parameters were measured and correlated with the Ki-67 labelling index (LI). The Ki-67 LI was categorised as high (> 50%) or low (≤ 50%).
RESULTS: The K and f values were positively correlated with the Ki-67 LI (rho = 0.295~0.532), whereas the ADC, Dk and D values were negatively correlated with the Ki-67 LI (rho = -0.443~-0.277). The ADC, Dk and D values were lower, whereas the K value was higher in sinonasal malignancies with a high Ki-67 LI than in those in a low Ki-67 LI (all p < 0.05). A higher maximum K value (Kmax > 0.977) independently predicted a high Ki-67 status [odds ratio (OR) = 7.614; 95% confidence interval (CI) = 2.197-38.674; p = 0.017].
CONCLUSION: ADC, Dk, K, D and f are correlated with Ki-67 LI. Kmax is the strongest independent factor for predicting Ki-67 status. KEY POINTS: • DWI-derived parameters from different models are capable of providing different pathophysiological information. • DWI, DKI and IVIM parameters are associated with Ki-67 proliferation status. • K max derived from DKI is the strongest independent factor for the prediction of Ki-67 proliferation status.

Entities:  

Keywords:  Diffusion magnetic resonance imaging; Immunohistochemistry; Magnetic resonance imaging; Neoplasms; Prognosis

Mesh:

Substances:

Year:  2018        PMID: 29383521     DOI: 10.1007/s00330-017-5286-x

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


  38 in total

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Authors:  Kun Sun; Xiaosong Chen; Weimin Chai; Xiaochun Fei; Caixia Fu; Xu Yan; Ying Zhan; Kemin Chen; Kunwei Shen; Fuhua Yan
Journal:  Radiology       Date:  2015-05-04       Impact factor: 11.105

2.  Salivary gland tumors: use of intravoxel incoherent motion MR imaging for assessment of diffusion and perfusion for the differentiation of benign from malignant tumors.

Authors:  Misa Sumi; Marc Van Cauteren; Tadateru Sumi; Makoto Obara; Yoko Ichikawa; Takashi Nakamura
Journal:  Radiology       Date:  2012-03-23       Impact factor: 11.105

Review 3.  Sinonasal tumors: a clinicopathologic update of selected tumors.

Authors:  Pieter J Slootweg; Alfio Ferlito; Antonio Cardesa; Lester D R Thompson; Jennifer L Hunt; Primož Strojan; Robert P Takes; Asterios Triantafyllou; Julia A Woolgar; Alessandra Rinaldo; Kenneth O Devaney; Leon Barnes
Journal:  Eur Arch Otorhinolaryngol       Date:  2012-05-18       Impact factor: 2.503

4.  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
Journal:  Magn Reson Med       Date:  2005-06       Impact factor: 4.668

5.  Diffusion-weighted MR imaging in laryngeal and hypopharyngeal carcinoma: association between apparent diffusion coefficient and histologic findings.

Authors:  Juliette P Driessen; Joana Caldas-Magalhaes; Luuk M Janssen; Frank A Pameijer; Nina Kooij; Chris H J Terhaard; Wilko Grolman; Marielle E P Philippens
Journal:  Radiology       Date:  2014-04-17       Impact factor: 11.105

6.  Clincopathological analysis of olfactory neuroblastoma.

Authors:  Shintaro Fukushima; Yasuo Sugita; Daisuke Niino; Hiroyuki Mihashi; Koichi Ohshima
Journal:  Brain Tumor Pathol       Date:  2012-02-14       Impact factor: 3.298

7.  Tumor Heterogeneity in Lung Cancer: Assessment with Dynamic Contrast-enhanced MR Imaging.

Authors:  Soon Ho Yoon; Chang Min Park; Sang Joon Park; Jeong-Hwa Yoon; Seokyung Hahn; Jin Mo Goo
Journal:  Radiology       Date:  2016-03-31       Impact factor: 11.105

Review 8.  Radiologic Features of Sinonasal Tumors.

Authors:  Kelly K Koeller
Journal:  Head Neck Pathol       Date:  2016-02-01

9.  Clinical and biological prognostic factors in 179 cases with sinonasal carcinoma treated in the Italian Piedmont region.

Authors:  Mario Airoldi; Massimiliano Garzaro; Guido Valente; Carlo Mamo; Antonella Bena; Carlo Giordano; Giancarlo Pecorari; Pietro Gabriele; Anna Maria Gabriele; Fabio Beatrice
Journal:  Oncology       Date:  2009-03-04       Impact factor: 2.935

10.  Non-Gaussian analysis of diffusion weighted imaging in head and neck at 3T: a pilot study in patients with nasopharyngeal carcinoma.

Authors:  Jing Yuan; David Ka Wai Yeung; Greta S P Mok; Kunwar S Bhatia; Yi-Xiang J Wang; Anil T Ahuja; Ann D King
Journal:  PLoS One       Date:  2014-01-23       Impact factor: 3.240

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  10 in total

1.  DWI and IVIM are predictors of Ki67 proliferation index: direct comparison of MRI images and pathological slices in a murine model of rhabdomyosarcoma.

Authors:  Yuan Yuan; Dewei Zeng; Yajie Liu; Juan Tao; Yu Zhang; Jie Yang; Tsendjav Lkhagvadorj; Zhenzhen Yin; Shaowu Wang
Journal:  Eur Radiol       Date:  2019-11-08       Impact factor: 5.315

2.  Differentiating between malignant and benign renal tumors: do IVIM and diffusion kurtosis imaging perform better than DWI?

Authors:  Yuqin Ding; Qinxuan Tan; Wei Mao; Chenchen Dai; Xiaoyi Hu; Jun Hou; Mengsu Zeng; Jianjun Zhou
Journal:  Eur Radiol       Date:  2019-06-03       Impact factor: 5.315

3.  Apparent diffusion coefficient (ADC) histogram analysis: differentiation of benign from malignant parotid gland tumors using readout-segmented diffusion-weighted imaging.

Authors:  Zanxia Zhang; Chengru Song; Yong Zhang; Baohong Wen; Jinxia Zhu; Jingliang Cheng
Journal:  Dentomaxillofac Radiol       Date:  2019-07-09       Impact factor: 2.419

4.  Multi-parametric MRI-based radiomics signature for preoperative prediction of Ki-67 proliferation status in sinonasal malignancies: a two-centre study.

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Journal:  Eur Radiol       Date:  2022-06-10       Impact factor: 7.034

5.  Monoexponential, biexponential and diffusion kurtosis MR imaging models: quantitative biomarkers in the diagnosis of placenta accreta spectrum disorders.

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6.  Three-dimensional pulsed continuous arterial spin labeling and intravoxel incoherent motion imaging of nasopharyngeal carcinoma: correlations with Ki-67 proliferation status.

Authors:  Wenxiu Wu; Guihua Jiang; Zhifeng Xu; Ruoning Wang; Aizhen Pan; Mingyong Gao; Tian Yu; Linwen Huang; Qiang Quan; Jin Li
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7.  Differential diagnosis of sinonasal extranodal NK/T cell lymphoma and diffuse large B cell lymphoma on MRI.

Authors:  Yun Chen; Xinyan Wang; Long Li; Wei Li; Junfang Xian
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8.  A Correlative Study Between IVIM-DWI Parameters and the Expression Levels of Ang-2 and TKT in Hepatocellular Carcinoma.

Authors:  Jing Zheng; Xue Qin Gong; Yun Yun Tao; Ran Wang; Gang Yang; Jing Dong Li; Tian Ren; Zu Mao Li; Cui Yang; Wei Cheng Wang; Lin Yang; Xiao Ming Zhang
Journal:  Front Oncol       Date:  2021-01-15       Impact factor: 6.244

9.  Predictive Ki-67 Proliferation Index of Cervical Squamous Cell Carcinoma Based on IVIM-DWI Combined with Texture Features.

Authors:  Cuiping Li; Mingxue Zheng; Xiaomin Zheng; Xin Fang; Jiangning Dong; Chuanbin Wang; Tingting Wang
Journal:  Contrast Media Mol Imaging       Date:  2021-01-14       Impact factor: 3.161

10.  Standard diffusion-weighted, diffusion kurtosis and intravoxel incoherent motion MR imaging of the whole placenta: a pilot study of volumetric analysis.

Authors:  Tao Lu; Yishuang Wang; Aiwen Guo; Cui Wei; Yazheng Chen; Shaoyu Wang; Bin Song
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  10 in total

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