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Proposal for a New Liver Tumor Classification Method in MRI1Fujita Health University: Fujita Ika Daigaku, Toyoake, Japan, 2Division of Health Sciences, Graduate School of Medical Sciences, Kanazawa University, Kanazawa, Japan, 3Tokushima Bunri University, Sanuki, Japan
Synopsis
Motivation: Liver tumors could be classified with the help of machine learning or other methods based solely on changes in Gd-EOB-DTPA uptake over time.
Goal(s): To evaluate the possibility of classifying liver tumor types using changes in liver and tumor contrast (Q-LTC) over time.
Approach: Liver tumors (HCC, metastasis, and hemangiomas) were classified. The rate of change in Q-LTC were calculated using images obtained at 3, 10, and 15 min after Gd-EOB-DTPA administration.
Results: The rate of change in Q-LTC over time tended to be higher in HCC, metastasis, and hemangioma, in that order; therefore, its potential use in liver tumor classification.
Impact: To reduce the burden on patients caused by extended examination time, we performed liver tumors classification using simple liver and tumor contrast based on the liver function, during routine clinical studies without requiring additional specialized imaging.
Background
It is important to characterize liver lesions, which include benign and malignant liver tumors, in liver imaging. Particular attention should be paid when benign and malignant lesions coexist, for example, when patients with known extrahepatic malignancies are evaluated for liver metastases [1], and metastatic involvement of the liver in extrahepatic malignant disease significantly affects therapeutic approach in many cases [2]. Besides, hepatocellular carcinoma (HCC) histological grade is an independent predictor of postoperative recurrence [3].Thus, accurately characterizing liver lesions is crucial for effective evaluation of liver tumors; moreover, characterization of focal liver lesions is important for treatment planning for patients with liver tumors [4].Purpose
We evaluated the possibility of classifying liver tumor types and differentiating hepatocellular carcinoma (HCC) based on changes in liver and tumor contrast over time in the late phases of a dynamic study to the hepatobiliary phase based on liver function, which is routinely performed in clinical studies.Methods
Overall, 147 patients with tumors (n = 165), including HCC, metastasis, and hemangiomas, were analyzed using 3.0T magnetic resonance imaging. Liver tumor types were classified based on albumin–bilirubin (ALBI) grade as a liver function [5]. The rate of change in Quantitative liver tumor contrast (Q-LTC) (%) was calculated using images obtained at 3, 10, and 15 min after gadolinium-ethoxybenzyl-diethylenetriamine penta-acetic acid (Gd-EOB-DTPA) administration.Results
Kruskal–Wallis tests revealed significant differences in Q-LTC at 3 min (P < 0.01) and 10 min (P < 0.05) after contrast in ALBI grade 1. Q-LTC per minute increased over time in the order of HCC, metastasis, and hemangioma. The rate of change in Q-LTC (%) was significantly different in all combinations in ALBI grade 1 (P < 0.01), but there were no significant differences in ALBI grade 2.Discussion
This preliminary study suggests that Q-LTC has the potential to provide diagnostic support to determine liver tumor types. If our results are equivalent or superior to those of multiparametric methods, liver tumors may be diagnosed without any images (e.g., T2WI, DWI, or the other additional images) with the help of machine learning or other methods based solely on changes in Gd-EOB-DTPA uptake over time. However, given that the results differ depending on ALBI grade, it will be necessary to provide not only images but also blood test data of liver function.Conclusion
The rate of change in Q-LTC over time (%) tended to be higher in HCC, metastasis, and hemangioma, in that order. In particular, the rate of change in Q-LTC over time (%) indicated its potential use in liver tumor classification.Acknowledgements
No acknowledgement found.References
[1] Wang YX. Superparamagnetic iron oxide based MRI contrast agents: current status of clinical application. Quant Imaging Med Surg. 2011;1:35–40.
[2] Zech CJ, Herrmann KA, Reser MF, et al. MR Imaging in Patient with Suspected Liver Metastases: Value of Liver-specific Contrast Agent Gd-EOB-DTPA. Magn Reason Med Sci. 2007;6:43–52.
[3] Jonas S, Bechstein WO, Steinmüller T, et al. Vascular invasion and histopathologic grading determine outcome after liver transplantation for hepatocellular carcinoma in cirrhosis. Hepatology. 2001;33:1080–1086.
[4] Hamm B, Thoeni RF, Gould RG, et al. Focal liver lesions: characterization with nonenhanced and dynamic contrast material-enhanced MR imaging. Radiology. 1994;190:417–423.
[5] Johnson PJ, Berhane S, Kagebayashi C, et al. Assessment of liver function in patients with hepatocellular carcinoma: a new evidence-based approach—the ALBI grade. J Clin Oncol. 2015;33:550–558.
Figures
Rate of change in Q-LTC (%) over time for among different tumor types.
ALBI, albumin–bilirubin grade; HCC, hepatocellular carcinoma