Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/105297
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dc.contributorDepartment of Health Technology and Informatics-
dc.contributorDepartment of Applied Biology and Chemical Technology-
dc.creatorChan, LWC-
dc.creatorWong, SCC-
dc.creatorCho, WCS-
dc.creatorHuang, M-
dc.creatorZhang, F-
dc.creatorChui, ML-
dc.creatorLai, UNY-
dc.creatorChan, TYK-
dc.creatorCheung, ZHC-
dc.creatorCheung, JCY-
dc.creatorTang, KF-
dc.creatorTse, ML-
dc.creatorWong, HK-
dc.creatorKwok, HMF-
dc.creatorShen, X-
dc.creatorZhang, S-
dc.creatorChiu, KWH-
dc.date.accessioned2024-04-12T06:51:24Z-
dc.date.available2024-04-12T06:51:24Z-
dc.identifier.urihttp://hdl.handle.net/10397/105297-
dc.language.isoenen_US
dc.publisherMDPI AGen_US
dc.rights© 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).en_US
dc.rightsThe following publication Chan LWC, Wong SCC, Cho WCS, Huang M, Zhang F, Chui ML, Lai UNY, Chan TYK, Cheung ZHC, Cheung JCY, et al. Primary Tumor Radiomic Model for Identifying Extrahepatic Metastasis of Hepatocellular Carcinoma Based on Contrast Enhanced Computed Tomography. Diagnostics. 2023; 13(1):102 is available at https://doi.org/10.3390/diagnostics13010102.en_US
dc.subjectClinical decision-makingen_US
dc.subjectComputed tomographyen_US
dc.subjectExtrahepatic metastasisen_US
dc.subjectHepatocellular carcinomaen_US
dc.subjectMachine learningen_US
dc.subjectOversamplingen_US
dc.subjectRadiomicsen_US
dc.titlePrimary tumor radiomic model for identifying extrahepatic metastasis of hepatocellular carcinoma based on contrast enhanced computed tomographyen_US
dc.typeJournal/Magazine Articleen_US
dc.identifier.volume13-
dc.identifier.issue1-
dc.identifier.doi10.3390/diagnostics13010102-
dcterms.abstractThis study aimed to identify radiomic features of primary tumor and develop a model for indicating extrahepatic metastasis of hepatocellular carcinoma (HCC). Contrast-enhanced computed tomographic (CT) images of 177 HCC cases, including 26 metastatic (MET) and 151 non-metastatic (non-MET), were retrospectively collected and analyzed. For each case, 851 radiomic features, which quantify shape, intensity, texture, and heterogeneity within the segmented volume of the largest HCC tumor in arterial phase, were extracted using Pyradiomics. The dataset was randomly split into training and test sets. Synthetic Minority Oversampling Technique (SMOTE) was performed to augment the training set to 145 MET and 145 non-MET cases. The test set consists of six MET and six non-MET cases. The external validation set is comprised of 20 MET and 25 non-MET cases collected from an independent clinical unit. Logistic regression and support vector machine (SVM) models were identified based on the features selected using the stepwise forward method while the deep convolution neural network, visual geometry group 16 (VGG16), was trained using CT images directly. Grey-level size zone matrix (GLSZM) features constitute four of eight selected predictors of metastasis due to their perceptiveness to the tumor heterogeneity. The radiomic logistic regression model yielded an area under receiver operating characteristic curve (AUROC) of 0.944 on the test set and an AUROC of 0.744 on the external validation set. Logistic regression revealed no significant difference with SVM in the performance and outperformed VGG16 significantly. As extrahepatic metastasis workups, such as chest CT and bone scintigraphy, are standard but exhaustive, radiomic model facilitates a cost-effective method for stratifying HCC patients into eligibility groups of these workups.-
dcterms.accessRightsopen accessen_US
dcterms.bibliographicCitationDiagnostics, Jan. 2023, v. 13, no. 1, 102-
dcterms.isPartOfDiagnostics-
dcterms.issued2023-01-
dc.identifier.scopus2-s2.0-85145907359-
dc.identifier.eissn2075-4418-
dc.identifier.artn102-
dc.description.validate202403 bcvc-
dc.description.oaVersion of Recorden_US
dc.identifier.FolderNumberOA_Scopus/WOSen_US
dc.description.fundingSourceOthersen_US
dc.description.fundingTextHealth and Medical Research Fund; Huawei Collaborative Research Fund, PolyUen_US
dc.description.pubStatusPublisheden_US
dc.description.oaCategoryCCen_US
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