The Value of Bi-exponential and Non-Gaussian Distribution Diffusion-weighted Imaging in the Differentiation of Recurrent Soft Tissue Neoplasms and Post-surgical Changes

Overview

Journal Ann Transl Med

Publisher AME Publishing Company

Date 2020 Dec 14

PMID 33313102

Citations 1

Authors

Peian Hu

Shengjian Zhang

Zhengrong Zhou

Affiliations

Soon will be listed here.

Abstract

Background: Many researches focused on the quantitative mono-exponential diffusion-weighted imaging (DWI) in the assessment of soft tissue neoplasms (STN), but few focused on the value of bi-exponential and non-Gaussian DWI in the application of Recurrent Soft Tissue Neoplasms (RSTN). This study aimed to explore the feasibility of bi-exponential decay and non-Gaussian distribution DWI in the differentiation of RSTN and Post-Surgery Changes (PSC), and compared with mono-exponential DWI.

Methods: The clinical, mono-exponential, bi-exponential [intravoxel incoherent motion (IVIM)] and non-Gaussian [diffusion kurtosis imaging (DKI)] DWI imaging of a cohort of 27 patients [15 RSTN (22 masses), and 12 PSC (12 lesions)] with 34 masses, from Nov 01 2017 to Sep 30 2018, were reviewed. The differences of apparent diffusion coefficient (ADC), true diffusion coefficient (D), pseudodiffusion coefficient (D*), perfusion fraction (f), mean diffusivity (MD), and mean kurtosis (MK) values were compared between RSTN and PSC groups. The mono-, bi-exponential, and non-Gaussian distribution based predictive models for RSTN and PSC were built and compared. ROC curves were generated and compared by the DeLong test.

Results: Intra-class correlation coefficient (ICC) of all IVIM/DKI parameters was high (≥0.841). There were significant differences in ADC, D, f, MD, and MK values between RSTN and PSC, but no difference in D* value. The ADC_IVIM, D, f and MD values of RSTN were lower than those of PSC, but with higher MK value. The ADC_IVIM and D values did better than f value in differentiating these two groups (P<0.05). While there was no significant difference in AUCs among ADC_DKI, MD, and MK values. Also, no significant difference was detected in AUCs between bi-exponential and mono-exponential (P=0.38), or between mono-exponential and non-Gaussian distribution based prediction models (P=0.09).

Conclusions: ADC, D, f, MD, and MK values can be used in the differentiation of RSTN and PSC.

Citing Articles

Virtual Biopsy in Soft Tissue Sarcoma. How Close Are We?.

Arthur A, Johnston E, Winfield J, Blackledge M, Jones R, Huang P Front Oncol. 2022; 12:892620.

PMID: 35847882 PMC: 9286756. DOI: 10.3389/fonc.2022.892620.

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