Preoperative assessment of microvascular invasion of hepatocellular carcinoma using non-Gaussian diffusion-weighted imaging with a fractional order calculus model: A pilot study

Background: To date, there are inconsistent data about relationships between diffusion-weighted imaging (DWI) and tumor grading/microvascular invasion (MVI) in hepatocellular carcinoma (HCC). Our purpose was to systematize the reported results regarding the role of DWI in prediction of tumor grading/MVI in HCC. Method: MEDLINE library, Scopus, and Embase data bases were screened up to December 2019. Overall, 29 studies with 2,715 tumors were included into the analysis. There were 20 studies regarding DWI and tumor grading, 8 studies about DWI and MVI, and 1 study investigated DWI, tumor grading, and MVI in HCC. Results: In 21 studies (1,799 tumors), mean apparent diffusion coefficient (ADC) values (ADCmean) were used for distinguishing HCCs. ADCmean of G1–3 lesions overlapped significantly. In 4 studies (461 lesions), minimum ADC (ADCmin) was used. ADCmin values in G1/2 lesions were over 0.80 × 10−3 mm2/s and in G3 tumors below 0.80 × 10−3 mm2/s. In 4 studies (241 tumors), true diffusion (D) was reported. A significant overlapping of D values between G1, G2, and G3 groups was found. ADCmean and MVI were analyzed in 9 studies (1,059 HCCs). ADCmean values of MIV+/MVI− lesions overlapped significantly. ADCmin was used in 4 studies (672 lesions). ADCmin values of MVI+ tumors were in the area under 1.00 × 10−3 mm2/s. In 3 studies (227 tumors), D was used. Also, D values of MVI+ lesions were predominantly in the area under 1.00 × 10−3 mm2/s. Conclusion: ADCmin reflects tumor grading, and ADCmin and D predict MVI in HCC. Therefore, these DWI parameters should be estimated for every HCC lesion for pretreatment tumor stratification. ADCmean cannot predict tumor grading/MVI in HCC.

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Microvascular invasion in hepatocellular carcinoma: is it predictable with a new, preoperative application of diffusion-weighted imaging?

Clinical Imaging ◽

10.1016/j.clinimag.2016.10.004 ◽

2017 ◽

Vol 41 ◽

pp. 101-105 ◽

Cited By ~ 13

Author(s):

Chun Yang ◽

Heqing Wang ◽

Ruofan Sheng ◽

Yuan Ji ◽

Shengxiang Rao ◽

...

Keyword(s):

Hepatocellular Carcinoma ◽

Diffusion Weighted Imaging ◽

Microvascular Invasion ◽

Diffusion Weighted

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Differentiation of salivary gland tumor using diffusion-weighted imaging with a fractional order calculus model

British Journal of Radiology ◽

10.1259/bjr.20200052 ◽

2020 ◽

Vol 93 (1113) ◽

pp. 20200052

Author(s):

Wei Chen ◽

Liu-Ning Zhu ◽

Yong-Ming Dai ◽

Jia-Suo Jiang ◽

Shou-Shan Bu ◽

...

Keyword(s):

Salivary Gland ◽

Diffusion Model ◽

Fractional Order ◽

Diffusion Weighted Imaging ◽

Diagnostic Performance ◽

Salivary Gland Tumors ◽

Characteristic Analysis ◽

Fractional Order Calculus ◽

Diffusion Weighted ◽

Significant Difference

Objective: To evaluate the feasibility of using imaging parameters (D, β and μ) obtained from fractional order calculus (FROC) diffusion model to differentiate salivary gland tumors. Methods: 15 b-value (0–2000 s/mm2) diffusion-weighted imaging (DWI) was scanned in 62 patients with salivary gland tumors (47 benign and 15 malignant). Diffusion coefficient D, fractional order parameter β (which correlates with tissue heterogeneity) and a microstructural quantity μ of the solid portion within the tumor were calculated, and compared between benign and malignant groups, or among pleomorphic adenoma (PA), Warthin’s tumor (WT), and malignant tumor (MT) groups. Performance of FROC parameters for differentiation was assessed using receiver operating characteristic analysis. Results: None of the FROC parameters exhibited significant differences between benign and malignant group (D, p = 0.150; β, p = 0.967; μ, p = 0.693). WT showed significantly lower D (p < 0.001) and β (p < 0.001), while higher μ (p = 0.001) than PA. Combination of D, β and μ showed optimal diagnostic performance (area under the curve, AUC, 0.998). MT showed significantly lower D (p = 0.001) and β (p = 0.025) than PA, while no significant difference was found on μ (p = 0.064). Combination of D and β showed optimal diagnostic performance (AUC, 0.933). Significant difference was found on β (p = 0.027) between MT and WT, while not on D (p = 0.806) and μ (p = 0.789). Setting a βof 0.615 as the cut-off value, optimal diagnostic performance could be obtained (AUC = 0.806). Conclusion: A non-Gaussian FROC diffusion model can serve as a noninvasive and quantitative imaging technique for differentiating salivary gland tumors. Advances in knowledge: (1) PA showed higher D and β and lower μ than WT. (2) PA had higher D and β than MT. (3) WT demonstrated lower β than MT. (4) β, as a new FROC parameter, could offer an added value to the differentiation.

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