Habitat Radiomics and Deep Learning on Gadoxetic Acid-Enhanced MRI for Noninvasive Assessment of CK19 Expression and Recurrence-Free Survival in Hepatocellular Carcinoma

Weihao Chen¹Jingcheng Hu¹Mingzhan Du¹Tao Zhang²Chunyan Gu²Qian Wu¹Yanfen Fan¹Ximing Wang¹Yixing Yu^1*Chunhong Hu^1*

• ¹First Affiliated Hospital of Soochow University, Suzhou, China
• ²The Third People's Hospital of Nantong, Nantong, China

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.