Abstract
PURPOSE: Few studies have explored the value of radiomics signatures in predicting immunohistochemical (IHC) staining markers. This study aimed to investigate and validate radiomics models based on the Kupffer phase of Sonazoid contrast-enhanced intraoperative ultrasonography (S-CEUS) images for predicting IHC marker expression in hepatocellular carcinoma (HCC). METHOD: Overall, 113 consecutive patients diagnosed with HCC between November 2019 and May 2023 were retrospectively analyzed. Histopathological assessment included IHC staining for GS, CD10, GPC3, and HSP70. Radiomic features extracted from S-CEUS images were selected and analyzed. A Naïve Bayes classifier was employed to predict IHC marker expression in HCC, using selected clinical biomarkers and radiomic features. RESULTS: For GPC3, the radiomics classifier achieved a macro-average area under the receiver operating characteristic curve (AUC) of 0.700, indicating strong performance. For GS, both radiomics and combined clinical-radiomics classifiers exhibited strong discrimination (AUCs: 0.870 and 0.882, respectively). The radiomics classifier outperformed clinical biomarkers (total and direct bilirubin) in predicting CD10, with a macro-average AUC of 0.834. However, its accuracy decreased for higher HSP70 marker expression levels (AUC: 0.694). These findings underscore the consistent effectiveness of radiomics across different IHC markers when compared to traditional clinical approaches. CONCLUSIONS: The Kupffer phase in the S-CEUS-based radiomics signature is an excellent biomarker for predicting IHC marker expression in patients with HCC.