Biparametric magnetic resonance imaging-based radiomic and deep learning models for predicting Ki-67 risk stratification in hepatocellular carcinoma.

Background: Hepatocellular carcinoma (HCC) is a prevalent and life-threatening cancer with increasing incidence worldwide. High Ki-67 risk stratification is closely associated with higher recurrence rates and worse outcomes following curative therapies in patients with HCC. However, the performance of radiomic and deep transfer learning (DTL) models derived from biparametric magnetic resonance imaging (bpMRI) in predicting Ki-67 risk stratification and recurrence-free survival (RFS) in patients with HCC remains limited.

Aim: To develop a nomogram model integrating bpMRI-based radiomic and DTL signatures for predicting Ki-67 risk stratification and RFS in patients with HCC.

Methods: This study included 198 patients with histopathologically confirmed HCC who underwent preoperative bpMRI. Ki-67 risk stratification was categorized as high (> 20%) or low (≤ 20%) according to immunohistochemical staining. Radiomic and DTL signatures were extracted from the T2-weighted and arterial-phase images and combined through a random forest algorithm to establish radiomic and DTL models, respectively. Multivariate regression analysis identified clinical risk factors for high Ki-67 risk stratification, and a predictive nomogram model was developed.

Results: A nonsmooth margin and the absence of an enhanced capsule were independent factors for high Ki-67 risk stratification. The area under the curve (AUC) of the clinical model was 0.77, while those of the radiomic and DTL models were 0.81 and 0.87, respectively, for the prediction of high Ki-67 risk stratification, and the nomogram model achieved a better AUC of 0.92. The median RFS times for patients with high and low Ki-67 risk stratification were 33.00 months and 66.73 months, respectively (P < 0.001). Additionally, patients who were predicted to have high Ki-67 risk stratification by the nomogram model had a lower median RFS than those who were predicted to have low Ki-67 risk stratification (33.53 vs 66.74 months, P = 0.007).

Conclusion: Our developed nomogram model demonstrated good performance in predicting Ki-67 risk stratification and predicting survival outcomes in patients with HCC.