Hepatocellular carcinoma pathologic grade prediction using radiomics and machine learning models of gadoxetic acid-enhanced MRI: a two-center study
- Authors
- Han, Yeo Eun; Cho, Yongwon; Kim, Min Ju; Park, Beom Jin; Sung, Deuk Jae; Han, Na Yeon; Sim, Ki Choon; Park, Yang Shin; Park, Bit Na
- Issue Date
- Jan-2023
- Publisher
- Springer New York
- Keywords
- Carcinoma; Hepatocellular; Machine learning; Magnetic resonance imaging; Neoplasm grading
- Citation
- Abdominal Radiology, v.48, no.1, pp 244 - 256
- Pages
- 13
- Indexed
- SCIE
SCOPUS
- Journal Title
- Abdominal Radiology
- Volume
- 48
- Number
- 1
- Start Page
- 244
- End Page
- 256
- URI
- https://scholarworks.korea.ac.kr/kumedicine/handle/2021.sw.kumedicine/61580
- DOI
- 10.1007/s00261-022-03679-y
- ISSN
- 2366-004X
2366-0058
- Abstract
- Purpose
To develop a radiomics-based hepatocellular carcinoma (HCC) grade classifier model based on data from gadoxetic acid-enhanced MRI.
Methods
This retrospective study included 137 patients who underwent hepatectomy for a single HCC and gadoxetic acid-enhanced MRI within 60 days before surgery. HCC grade was categorized as low or high (modified Edmondson–Steiner grade I–II vs. III–IV). We used the hepatobiliary phase (HBP), portal venous phase, T2-weighted image(T2WI), and T1-weighted image(T1WI). From the volume of interest in HCC, 833 radiomic features were extracted. Radiomic and clinical features were selected using a random forest regressor, and the classification model was trained and validated using a random forest classifier and tenfold stratified cross-validation. Eight models were developed using the radiomic features alone or by combining the radiomic and clinical features. Models were validated with internal enrolled data (internal validation) and a dataset (28 patients) at a separate institution (external validation). The area under the curve (AUC) of the validation results was compared using the DeLong test.
Results
In internal and external validation, the HBP radiomics-only model showed the highest AUC (internal 0.80 ± 0.09, external 0.70 ± 0.09). In external validation, all models showed lower AUC than those for internal validation, while the T2WI and T1WI models failed to predict the HCC grade (AUC 0.30–0.58) in contrast to the internal validation results (AUC 0.67–0.78).
Conclusion
The radiomics-based machine learning model from gadoxetic acid-enhanced liver MRI could distinguish between low- and high-grade HCCs. The radiomics-only HBP model showed the best AUC among the eight models, good performance in internal validation, and fair performance in external validation.
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Collections - 4. Research institute > Institute of Human Behavior and Genetics > 1. Journal Articles
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