Evaluation of Multiple Prognostic Factors of Hepatocellular Carcinoma with Intra-Voxel Incoherent Motions Imaging by Extracting the Histogram Metrics
Authors Shi G, Han X, Wang Q, Ding Y, Liu H, Zhang Y, Dai Y
Received 21 May 2020
Accepted for publication 26 June 2020
Published 20 July 2020 Volume 2020:12 Pages 6019—6031
Checked for plagiarism Yes
Review by Single anonymous peer review
Peer reviewer comments 2
Editor who approved publication: Dr Eileen O'Reilly
Gaofeng Shi,1,* Xue Han,1,* Qi Wang,1 Yan Ding,1 Hui Liu,1 Yunfei Zhang,2 Yongming Dai2
1Department of Radiology, Fourth Hospital of Hebei Medical University, Shijiazhuang 050000, People’s Republic of China; 2Department of Research Collaboration Hospital (MRI), Central Research Institute, United Imaging Healthcare, Shanghai 201800, People’s Republic of China
*These authors contributed equally to this work
Correspondence: Hui Liu; Yunfei Zhang Tel +86-311-86095716
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Purpose: To predict multiple prognostic factors of HCC including histopathologic grade, the expression of Ki67 as well as capsule formation with intravoxel incoherent motions imaging by extracting the histogram metrics.
Patients and Methods: A total of 52 patients with HCC were recruited with the MR examinations undertaken at a 3T scanner. Histogram metrics were extracted from IVIM-derived parametric maps. Independent student t-test was performed to explore the differences in metrics across different subtypes of prognostic factors. Spearman correlation test was utilized to evaluate the correlations between the IVIM metrics and prognostic factors. ROC analysis was applied to evaluate the diagnostic performance.
Results: According to the independent student t-test, there were 18, 4, and 8 IVIM-derived histogram metrics showing the capability for differentiating the subtypes of histopathologic grade, Ki67, and capsule formation, respectively, with P-values of less than 0.05. Besides, there existed a lot of significant correlations between IVIM metrics and prognostic factors. Finally, by integrating different histogram metrics showing significant differences between various subgroups together via establishing logistic regression based diagnostic models, greatest diagnostic power was obtained for grading HCC (AUC=0.917), diagnosing patients with highly expressed Ki67 (AUC=0.861) and diagnosing patients with capsule formation (AUC=0.839).
Conclusion: Multiple prognostic factors including histopathologic grade, Ki67 expression status, and capsule formation can be accurately predicted with assistance of histogram metrics sourced from a single IVIM scan.
Keywords: diffusion magnetic resonance imaging, intravoxel incoherent motions, hepatocellular carcinoma, prognostic factors, histogram analysis
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