ArticleFrontiers in oncology2025
Habitat radiomics and deep learning on gadoxetic acid-enhanced MRI for noninvasive assessment of CK19 expression and recurrence-free survival in hepatocellular carcinoma.
Article in Frontiers in oncology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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2 citing papers in PubMed.
- MRI-based clinical-radiomics-habitat model for predicting prognosis of hepatocellular carcinoma patients treated with HAIC.Frontiers in oncology · 2026Article
- The translational paradox of AI in hepatocellular carcinoma: from algorithmic over-engineering to real-world clinical utility.Frontiers in oncology · 2026Review
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10 authors.
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Abstract
Objectives: To develop a non-invasive model for the preoperative prediction of Cytokeratin 19 (CK19) expression in hepatocellular carcinoma (HCC) based on clinical, radiologic, habitat radiomics, and deep learning features using gadoxetic acid-enhanced MRI, and to assess its utility for RFS risk stratification. Methods: In this retrospective study, 539 patients with HCC from two hospitals were divided into training (n = 266), internal (n = 114), and external (n = 159) test sets. Univariable and multivariable logistic regression analyses were conducted on clinical and radiologic features to develop a clinical-radiologic model. Habitat radiomics and deep learning (DL) features were extracted and selected to develop the Habitat and DL models, respectively. The DL-HR nomogram model incorporating clinical, radiologic, habitat radiomics, and deep learning features was developed and evaluated. The Kaplan-Meier survival analysis assessed recurrence-free survival (RFS) in the CK19-positive (CK19+) and CK19-negative (CK19-) patients. Results: AFP level and arterial phase (AP) enhancement were identified as independent predictors of CK19 expression. The DL-HR nomogram model showed superior performance compared to the clinical-radiologic model in both internal and external test sets (all P < 0.05). The AUCs of the DL-HR nomogram and clinical-radiologic models were 0.794 [95% CI: 0.708-0.864] vs. 0.615 [95% CI: 0.520-0.705] for the internal test set and 0.744 [95% CI: 0.669-0.810] vs. 0.600 [95% CI: 0.520-0.677] for the external test set, respectively. RFS was significantly different between the DL-HR nomogram model-predicted CK19+ and CK19- HCC patients across all sets (all P < 0.05). Conclusions: The DL-HR nomogram model integrating clinical, radiologic, habitat radiomics, and deep learning features effectively predicted the CK19 expression and served as an effective tool for RFS risk stratification in HCC.
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