Early prediction of adverse outcomes in liver cirrhosis using a CT-based multimodal deep learning model.
Purpose: Early-stage cirrhosis frequently presents without symptoms, making timely identification of high-risk patients challenging. We aimed to develop a deep learning-based triple-modal fusion liver cirrhosis network (TMF-LCNet) for the prediction of adverse outcomes, offering a promising tool to...
| Publicado en: | Abdominal Radiology Vol. 51; no. 1; pp. 137 - 151 |
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| Autores principales: | , , , , , , , , , |
| Formato: | Journal Article |
| Publicado: |
Springer Nature
Jan2026
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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=191138654&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 191138654 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 2366004X JT14 jtl: Abdominal Radiology issn: 2366004X maglogo: N pubinfo: dt: Jan2026 vid: 51 iid: 1 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 191138654 186192773 10.1007/s00261-025-05045-0 191138654 ppf: 137 ppct: 14 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Early prediction of adverse outcomes in liver cirrhosis using a CT-based multimodal deep learning model. aug: au: Xie, Nanai Liang, Yiwen Luo, Zixin Hu, Jing Ge, Ruiquan Wan, Xiang Wang, Changmiao Zou, Guannan Guo, Feng Jiang, Yi affil: https://ror.org/00t33hh48 Department of Radiology, The Second Affiliated Hospital, School of Medicine, The Chinese University of Hong Kong, Shenzhen & Longgang District People's Hospital of Shenzhen, Shenzhen, China sug: ab: Purpose: Early-stage cirrhosis frequently presents without symptoms, making timely identification of high-risk patients challenging. We aimed to develop a deep learning-based triple-modal fusion liver cirrhosis network (TMF-LCNet) for the prediction of adverse outcomes, offering a promising tool to enhance early risk assessment and improve clinical management strategies. Methods: This retrospective study included 243 patients with early-stage cirrhosis across two centers. Adverse outcomes were defined as the development of severe complications like ascites, hepatic encephalopathy and variceal bleeding. TMF-LCNet was developed by integrating three types of data: non-contrast abdominal CT images, radiomic features extracted from liver and spleen, and clinical text detailing laboratory parameters and adipose tissue composition measurements. TMF-LCNet was compared with conventional methods on the same dataset, and single-modality versions of TMF-LCNet were tested to determine the impact of each data type. Model effectiveness was measured using the area under the receiver operating characteristics curve (AUC) for discrimination, calibration curves for model fit, and decision curve analysis (DCA) for clinical utility. Results: TMF-LCNet demonstrated superior predictive performance compared to conventional image-based, radiomics-based, and multimodal methods, achieving an AUC of 0.797 in the training cohort (n = 184) and 0.747 in the external test cohort (n = 59). Only TMF-LCNet exhibited robust model calibration in both cohorts. Of the three data types, the imaging modality contributed the most, as the image-only version of TMF-LCNet achieved performance closest to the complete version (AUC = 0.723 and 0.716, respectively; p > 0.05). This was followed by the text modality, with radiomics contributing the least, a pattern consistent with the clinical utility trends observed in DCA. Conclusion: TMF-LCNet represents an accurate and robust tool for predicting adverse outcomes in early-stage cirrhosis by integrating multiple data types. It holds potential for early identification of high-risk patients, guiding timely interventions, and ultimately improving patient prognosis. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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