Background: The evaluation of tumor differentiation is an urgent clinical issue that would facilitate the establishment of individualized therapeutic strategies. Our aims were to develop a deep learning radiomics model based on computed tomography (CT) data for preoperative evaluation of hepatocellular carcinoma (HCC) differentiation (low vs high grade), and to preliminarily explore the biological basis of the radiomics model.
Methods: A total of 1234 HCC patients with contrast-enhanced CT images were recruited from two institutions. Radiomics features were extracted from preoperative venous-phase CT data and selected in terms of reproducibility, relevance, and redundancy. The random forest (RF) algorithm was applied to establish a radiomics signature. Meanwhile, the deep learning model was constructed based on a VGG network. Additionally, clinical characteristics of the subjects were used to construct a clinical model. Finally, the fused model integrated a tripartite prediction based on a logistic regression algorithm. A correlation analysis was performed to explore the association between radiomics features and biological variables based on multiomics levels.
Findings: The radiomics signature established with the RF algorithm comprised 25 radiomics features. The AUCs in the training, internal validation, and independent test cohorts were 0·82, 0·76, and 0·75, respectively, for the radiomics signature; 0·85, 0·81, and 0·75, respectively, for the deep learning model; and 0·89, 0·83, and 0·80, respectively, for the fused model. Multiomics analysis showed that the selected radiomics features contained abundant biological information that was related to tumor differentiation.
Interpretation: Our deep learning radiomics model can serve as a noninvasive tool of preoperative HCC differentiation evaluation to guide clinical decision-making and prognostic stratification.
Funding: National Key Research and Development Program of China, Natural Science Foundation of China, Zhejiang Provincial Education Department.
Declaration of Interest: The authors declare that they have no competing interests.
Ethical Approval: This study was approved by the institutional review boards of the First Affiliated Hospital, College of Medicine, Zhejiang University (Institution 1) and Ningbo Medical Center Lihuili Hospital (Institution 2), which waived the requirement for patients’ informed consent.
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