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Tytuł pozycji:

Magnetic Resonance Diffusion Kurtosis Imaging versus Diffusion-Weighted Imaging in Evaluating the Pathological Grade of Hepatocellular Carcinoma

Tytuł:
Magnetic Resonance Diffusion Kurtosis Imaging versus Diffusion-Weighted Imaging in Evaluating the Pathological Grade of Hepatocellular Carcinoma
Autorzy:
Wang GZ
Guo LF
Gao GH
Li Y
Wang XZ
Yuan ZG
Temat:
hepatocellular carcinoma
pathology
diffusion kurtosis imaging
diffusion-weighted imaging
predictor.
Neoplasms. Tumors. Oncology. Including cancer and carcinogens
RC254-282
Źródło:
Cancer Management and Research, Vol Volume 12, Pp 5147-5158 (2020)
Wydawca:
Dove Medical Press, 2020.
Rok publikacji:
2020
Kolekcja:
LCC:Neoplasms. Tumors. Oncology. Including cancer and carcinogens
Typ dokumentu:
article
Opis pliku:
electronic resource
Język:
English
ISSN:
1179-1322
Relacje:
https://www.dovepress.com/magnetic-resonance-diffusion-kurtosis-imaging-versus-diffusion-weighte-peer-reviewed-article-CMAR; https://doaj.org/toc/1179-1322
Dostęp URL:
https://doaj.org/article/31bcf1fd294647b99e99988360d3368a  Link otwiera się w nowym oknie
Numer akcesji:
edsdoj.31bcf1fd294647b99e99988360d3368a
Czasopismo naukowe
Guang-Zhi Wang,1,2 Ling-Fei Guo,3 Gui-Hua Gao,3 Yao Li,4 Xi-Zhen Wang,2 Zhen-Guo Yuan1,3 1Cheeloo College of Medicine, Shandong University, Jinan 250021, People’s Republic of China; 2Department of Medical Imaging Center, Affiliated Hospital, Weifang Medical University, Weifang 261053, People’s Republic of China; 3Department of MRI, Shandong Medical Imaging Research Institute, Cheeloo College of Medicine, Shandong University, Jinan 250021, People’s Republic of China; 4Zhucheng People’s Hospital Affiliated to Weifang Medical University, Weifang 262200, People’s Republic of ChinaCorrespondence: Zhen-Guo YuanShandong Medical Imaging Research Institute, Cheeloo College of Medicine, Shandong University, No. 324, Jing-wu road, Huaiyin District, Jinan 250021, People’s Republic of ChinaEmail yuanzg88@126.comPurpose: To investigate the diagnostic efficacy of diffusion kurtosis imaging (DKI) and conventional diffusion-weighted imaging (DWI) for pathological grading.Methods: From December 2015 to January 2017, consecutive patients suspected of having hepatocellular carcinoma (HCC) without prior treatment were prospectively enrolled in this study. MRI examinations were performed before surgical treatment. HCC patients confirmed by surgical pathology were included in the study. The mean diffusivity (MD) values, mean kurtosis (MK) values, and apparent diffusion coefficient (ADC) were calculated. The differences and correlations of these parameters among different pathological grades were analyzed. The diagnostic efficiency of DKI and DWI for predicting high-grade HCC was evaluated by receiver operating characteristic (ROC) curves. Logistic regression analyses were used to evaluate the predictive factors for pathological grade.Results: A total of 128 patients (79 males and 49 females, age: 56.9± 10.9 years, range, 32– 80) with primary HCC were included: grade I: 22 (17.2%) patients, grade II: 37 (28.9%) patients, grade III: 43 (33.6%) patients, grade IV: 26 (20.3%) patients. The MK values of stage I, II, III, and IV were 0.86± 0.13, 1.06± 0.11, 1.27± 0.17, and 1.57± 0.13, respectively. The MK values were significantly higher in the high-grade group than in the low-grade group and were positively correlated with pathological grade (rho =0.7417, P< 0.001). The MK value demonstrated a larger area under the curve (AUC), with a value of 0.93 than the MD value, which had an AUC of 0.815 (P< 0.001), and ADC, which had an AUC of 0.662 (P=0.01). The MK value (> 1.19), ADC (≤ 1.29× 10– 3 mm2/s), and HBV (+) were independent predictors for the pathological grade of HCCs.Conclusion: The MK values derived from DKI and the ADC values obtained from traditional DWI were more valuable than the MD values in predicting the histological grade of HCCs and could potentially guide clinical treatment before surgery.Keywords: hepatocellular carcinoma, pathology, diffusion kurtosis imaging, diffusion-weighted imaging, predictor

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