Artificial intelligence for the prevention and clinical management of hepatocellular carcinoma

Abstract

Hepatocellular carcinoma (HCC) currently represents the fifth most common malignancy and the third-leading cause of cancer-related death worldwide, with incidence and mortality rates that are increasing. Recently, artificial intelligence (AI) has emerged as a unique opportunity to improve the full spectrum of HCC clinical care, by improving HCC risk prediction, diagnosis, and prognostication. AI approaches include computational search algorithms, machine learning (ML) and deep learning (DL) models. ML consists of a computer running repeated iterations of models, in order to progressively improve performance of a specific task, such as classifying an outcome. DL models are a subtype of ML, based on neural network structures that are inspired by the neuroanatomy of the human brain. A growing body of recent data now apply DL models to diverse data sources - including electronic health record data, imaging modalities, histopathology and molecular biomarkers - to improve the accuracy of HCC risk prediction, detection and prediction of treatment response. Despite the promise of these early results, future research is still needed to standardise AI data, and to improve both the generalisability and interpretability of results. If such challenges can be overcome, AI has the potential to profoundly change the way in which care is provided to patients with or at risk of HCC.

Keywords: Artificial intelligence; Deep learning; Liver cancer; Machine learning.

Figure 1.

Definitions of Artificial Intelligence (AI), Machine Learning (ML) and Deep Learning (DL)

Figure 2.. General concept of pipelines using neural networks.

Different input data are preprocessed in such a way that they can be used as input values for the training of a neural network. The neural network consists of one input layer, multiple hidden convolutional and / or multiple fully connected layers extracting features from the input data, and one output layer with nodes that refer to different labels. These networks can then - among others - be used to classify data or to predict therapy response or survival prognosis.

Figure 3.. Explainable Artificial Intelligence: example of pathology.

This virtual model is dedicated to the prediction of the tumor or non-tumor nature of images from liver digital slides. The aim of explainability is to better understand, through transparency, semantics and explanation, how the model makes its predictions. Transparency (1) consists in having an in-depth knowledge of the structure of the neural network and the activation status of its different neurons/nodes. Semantics will provide insights on the type of objects that results in the activation of particular parts of the network). Finally, explanation allows to understand how the association of different features impact the final prediction.

Figure 4.. Artificial intelligence could support doctors in decision making in tumor therapy in the future.

A) Current oncologic therapy pattern. After an initial first-line therapy, the tumor is evading therapy through resistance mechanisms. The following tumor growth is recognized during radiologic follow-up leading to therapy adjustment. B) Hypothetical, future, AI-supported therapy pattern. Initial, individualized first-line therapy decision, accounting for an AI-based recommendation. After an AI algorithm predicts progression of a tumor, doctors decide to adjust therapy before the tumor can develop resistance to therapy and grow again.