Hepatocellular carcinoma pathologic grade prediction using radiomics and machine learning models of gadoxetic acid-enhanced MRI: a two-center study
DC Field | Value | Language |
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dc.contributor.author | Han, Yeo Eun | - |
dc.contributor.author | Cho, Yongwon | - |
dc.contributor.author | Kim, Min Ju | - |
dc.contributor.author | Park, Beom Jin | - |
dc.contributor.author | Sung, Deuk Jae | - |
dc.contributor.author | Han, Na Yeon | - |
dc.contributor.author | Sim, Ki Choon | - |
dc.contributor.author | Park, Yang Shin | - |
dc.contributor.author | Park, Bit Na | - |
dc.date.accessioned | 2022-11-20T05:40:58Z | - |
dc.date.available | 2022-11-20T05:40:58Z | - |
dc.date.created | 2022-11-17 | - |
dc.date.issued | 2022 | - |
dc.identifier.issn | 2366-004X | - |
dc.identifier.uri | https://scholar.korea.ac.kr/handle/2021.sw.korea/146080 | - |
dc.description.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. [GRAPHICS] . | - |
dc.language | English | - |
dc.language.iso | en | - |
dc.publisher | SPRINGER | - |
dc.subject | TUMOR | - |
dc.subject | LIVER | - |
dc.subject | VALIDATION | - |
dc.subject | RECURRENCE | - |
dc.subject | FEATURES | - |
dc.title | Hepatocellular carcinoma pathologic grade prediction using radiomics and machine learning models of gadoxetic acid-enhanced MRI: a two-center study | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Cho, Yongwon | - |
dc.contributor.affiliatedAuthor | Han, Na Yeon | - |
dc.identifier.doi | 10.1007/s00261-022-03679-y | - |
dc.identifier.scopusid | 2-s2.0-85138504990 | - |
dc.identifier.wosid | 000857989900001 | - |
dc.identifier.bibliographicCitation | ABDOMINAL RADIOLOGY | - |
dc.relation.isPartOf | ABDOMINAL RADIOLOGY | - |
dc.citation.title | ABDOMINAL RADIOLOGY | - |
dc.type.rims | ART | - |
dc.type.docType | Article; Early Access | - |
dc.description.journalClass | 1 | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Radiology, Nuclear Medicine & Medical Imaging | - |
dc.relation.journalWebOfScienceCategory | Radiology, Nuclear Medicine & Medical Imaging | - |
dc.subject.keywordPlus | TUMOR | - |
dc.subject.keywordPlus | LIVER | - |
dc.subject.keywordPlus | VALIDATION | - |
dc.subject.keywordPlus | RECURRENCE | - |
dc.subject.keywordPlus | FEATURES | - |
dc.subject.keywordAuthor | Carcinoma | - |
dc.subject.keywordAuthor | Hepatocellular | - |
dc.subject.keywordAuthor | Machine learning | - |
dc.subject.keywordAuthor | Magnetic resonance imaging | - |
dc.subject.keywordAuthor | Neoplasm grading | - |
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