Prediction model of early recurrence of multimodal hepatocellular carcinoma with tensor fusion

Abstract

Abstract Objective. In oncology, clinical decision-making relies on a multitude of data modalities, including histopathological, radiological, and clinical factors. Despite the emergence of computer-aided multimodal decision-making systems for predicting hepatocellular carcinoma (HCC) recurrence post-hepatectomy, existing models often employ simplistic feature-level concatenation, leading to redundancy and suboptimal performance. Moreover, these models frequently lack effective integration with clinically relevant data and encounter challenges in integrating diverse scales and dimensions, as well as incorporating the liver background, which holds clinical significance but has been previously overlooked. Approach. To address these limitations, we propose two approaches. Firstly, we introduce the tensor fusion method to our model, which offers distinct advantages in handling multi-scale and multi-dimensional data fusion, potentially enhancing overall performance. Secondly, we pioneer the consideration of the liver background’s impact, integrating it into the feature extraction process using a deep learning segmentation-based algorithm. This innovative inclusion aligns the model more closely with real-world clinical scenarios, as the liver background may contain crucial information related to postoperative recurrence. Main results. We collected radiomics (MRI) and histopathological images from 176 cases diagnosed by experienced clinicians across two independent centers. Our proposed network underwent training and 5-fold cross-validation on this dataset before validation on an external test dataset comprising 40 cases. Ultimately, our model demonstrated outstanding performance in predicting early recurrence of HCC postoperatively, achieving an AUC of 0.883. Significance. These findings signify significant progress in addressing challenges related to multimodal data fusion and hold promise for more accurate clinical outcome predictions. In this study, we exploited global 3D liver background into modelling which is crucial to to the prognosis assessment and analyzed the whole liver background in addition to the tumor region. Both MRI images and histopathological images of HCC were fused at high-dimensional feature space using tensor techniques to solve cross-scale data integration issue.