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

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Abstract

Clinical decision-making in oncology involves multimodal data, encompassing histopathological, radiological, and clinical factors. Several computer-aided multimodal decision-making systems have emerged in recent years to predict the recurrence of hepatocellular carcinoma (HCC) after hepatectomy, but they tend to employ simplistic feature-level concatenation, resulting in redundancy and hampering overall performance. More notably, these models often lack an effective integration with clinical relevance. Particularly, they still face major challenges of integrating data from diverse scales and dimensions, and introducing a liver background, which are clinically significant but previously overlooked aspects.

 In addressing these challenges, we provide new insight in two areas. Firstly, we introduce the tensor fusion method into the model, which demonstrates unique advantages in handling the fusion of multi-scale and multi-dimensional data, thus potentially enhancing the model’s performance. Secondly, to our best knowledge, it’s a precedent to take the impact of the liver background into account. We innovatively incorporate the impact of the liver background into the feature extraction process by using a deep learning segmentation-based algorithm. This inclusion makes the model closer to real-world clinical scenarios, as the liver background may contain vital information related to postoperative recurrence.
 
 We collected radiomics (MRI) and histopathological images of 176 cases diagnosed by experienced clinicians from two independent centers. Our proposed network went through training and 5-fold cross-validation on the dataset of 176 cases and was subsequently validated on an independent external dataset of 40 cases. Finally, our proposed network exhibited excellent performance in predicting the postoperative early recurrence of HCC with an AUC of 0.883. These results suggest significant progress in addressing the challenges related to multimodal data fusion which provides potential value for more accurate predictions of clinical outcomes.© 2024 Institute of Physics and Engineering in Medicine.