Predicting the stages of liver fibrosis with multiphase CT radiomics based on volumetric features.
Purposes: To develop and externally validate a multiphase computed tomography (CT)-based machine learning (ML) model for staging liver fibrosis (LF) by using whole liver slices. Materials and methods: The development dataset comprised 232 patients with pathological analysis for LF, and the test data...
| Publicado en: | Abdominal Radiology Vol. 46; no. 8; pp. 3866 - 3877 |
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| Autores principales: | , , , , , , |
| Formato: | Journal Article |
| Publicado: |
Springer Nature
Aug2021
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=151456937&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 151456937 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 2366004X JT14 jtl: Abdominal Radiology issn: 2366004X maglogo: N pubinfo: dt: Aug2021 vid: 46 iid: 8 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 151456937 149405642 10.1007/s00261-021-03051-6 151456937 ppf: 3866 ppct: 11 formats: fmt: @attributes: type: P tig: atl: Predicting the stages of liver fibrosis with multiphase CT radiomics based on volumetric features. aug: au: Cui, Enming Long, Wansheng Wu, Juanhua Li, Qing Ma, Changyi Lei, Yi Lin, Fan affil: Department of Radiology, Jiangmen Central Hospital, Affiliated Jiangmen Hospital of SUN YAT-SEN University, Jiangmen Clinical Medical School of Guangdong Medical University, 23 Beijie Haibang Street, 529030, Jiangmen, People's Republic of China sug: ab: Purposes: To develop and externally validate a multiphase computed tomography (CT)-based machine learning (ML) model for staging liver fibrosis (LF) by using whole liver slices. Materials and methods: The development dataset comprised 232 patients with pathological analysis for LF, and the test dataset comprised 100 patients from an independent outside institution. Feature extraction was performed based on the precontrast (PCP), arterial (AP), portal vein (PVP) phase, and three-phase CT images. CatBoost was utilized for ML model investigation by using the features with good reproducibility. The diagnostic performance of ML models based on each single- and three-phase CT image was compared with that of radiologists' interpretations, the aminotransferase-to-platelet ratio index, and the fibrosis index based on four factors (FIB-4) by using the receiver operating characteristic curve with the area under the curve (AUC) value. Results: Although the ML model based on three-phase CT image (AUC = 0.65–0.80) achieved higher AUC value than that based on PCP (AUC = 0.56–0.69) and PVP (AUC = 0.51–0.74) in predicting various stage of LF, significant difference was not found. The best CT-based ML model (AUC = 0.65–0.80) outperformed the FIB-4 in differentiating advanced LF and cirrhosis and radiologists' interpretation (AUC = 0.50–0.76) in the diagnosis of significant and advanced LF. Conclusion: All PCP, PVP, and three-phase CT-based ML models can be an acceptable in assessing LF, and the performance of the PCP-based ML model is comparable to that of the enhanced CT image-based ML model. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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