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Assessment of Microvascular Invasion of Hepatocellular Carcinoma with Diffusion Kurtosis Imaging
Wen-Tao Wang1, Li Yang1, Zhao-Xia Yang1
1From the Department of Radiology, Zhongshan Hospital, Fudan University, and Shanghai Medical Imaging Institute, 180 Fenglin Rd, 200032 Shanghai, China (W.T.W., L.Y., Z.X.Y., X.X.H., Y.D., M.S.Z., S.X.R.); MR Collaboration NE Asia, Siemens Healthcare, Shanghai, China (X.Y.); Siemens Shenzhen Magnetic Resonance Ltd, Shenzhen, China (C.X.F.); and MR Application Predevelopment, Siemens Healthcare GmbH, Erlangen, Germany (R.G.).
Abstract:
Purpose To evaluate the potential role of diffusion kurtosis imaging and conventional magnetic resonance (MR) imaging findings including standard monoexponential model of diffusion-weighted imaging and morphologic features for preoperative prediction of microvascular invasion (MVI) of hepatocellular carcinoma (HCC). Materials and Methods Institutional review board approval and written informed consent were obtained. Between September 2015 and November 2016, 84 patients (median age, 54 years; range, 29-79 years) with 92 histopathologically confirmed HCCs (40 MVI-positive lesions and 52 MVI-negative lesions) were analyzed. Preoperative MR imaging examinations including diffusion kurtosis imaging (b values: 0, 200, 500, 1000, 1500, and 2000 sec/mm2) were performed and kurtosis, diffusivity, and apparent diffusion coefficient maps were calculated. Morphologic features of conventional MR images were also evaluated. Univariate and multivariate logistic regression analyses were used to evaluate the relative value of these parameters as potential predictors of MVI. Results Features significantly related to MVI of HCC at univariate analysis were increased mean kurtosis value (P < .001), decreased mean diffusivity value (P = .033) and apparent diffusion coefficient value (P = .011), and presence of infiltrative border with irregular shape (P = .005) and irregular circumferential enhancement (P = .026). At multivariate analysis, mean kurtosis value (odds ratio, 6.25; P = .001), as well as irregular circumferential enhancement (odds ratio, 6.92; P = .046), were independent risk factors for MVI of HCC. The mean kurtosis value for MVI of HCC showed an area under the receiver operating characteristic curve of 0.784 (optimal cutoff value was 0.917). Conclusion Higher mean kurtosis values in combination with irregular circumferential enhancement are potential predictive biomarkers for MVI of HCC. © RSNA, 2017.
Insights
Higher mean kurtosis values from diffusion kurtosis imaging and irregular enhancement on conventional MRI can help predict microvascular invasion (MVI) in hepatocellular carcinoma (HCC) before surgery.
Area of Science:
- Radiology
- Oncology
- Medical Imaging
Background:
- Microvascular invasion (MVI) is a key prognostic factor in hepatocellular carcinoma (HCC).
- Accurate preoperative prediction of MVI is crucial for treatment planning and patient outcomes.
- Conventional MRI has limitations in reliably detecting MVI.
Purpose of the Study:
- To assess the efficacy of diffusion kurtosis imaging (DKI) and conventional MRI features in predicting MVI in HCC.
- To identify imaging biomarkers for preoperative MVI risk stratification.
Main Methods:
- Retrospective analysis of 92 HCC cases (40 MVI-positive, 52 MVI-negative) using preoperative MRI.
- Acquisition of DKI data with multiple b values and calculation of kurtosis and diffusivity maps.
- Evaluation of conventional MRI morphologic features, including border and enhancement patterns.
Main Results:
- Increased mean kurtosis values and decreased diffusivity/ADC values were associated with MVI.
- Infiltrative borders and irregular circumferential enhancement were also linked to MVI.
- Multivariate analysis identified mean kurtosis value (OR, 6.25) and irregular circumferential enhancement (OR, 6.92) as independent predictors of MVI.
Conclusions:
- Higher mean kurtosis values and irregular circumferential enhancement are promising imaging biomarkers for preoperative MVI prediction in HCC.
- Combining DKI and conventional MRI features may improve MVI detection accuracy.
- These findings can aid in surgical decision-making and patient management for HCC.
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