Current updates in machine learning in the prediction of therapeutic outcome of hepatocellular carcinoma: what should we know?

Abstract

With the development of machine learning (ML) algorithms, a growing number of predictive models have been established for predicting the therapeutic outcome of patients with hepatocellular carcinoma (HCC) after various treatment modalities. By using the different combinations of clinical and radiological variables, ML algorithms can simulate human learning to detect hidden patterns within the data and play a critical role in artificial intelligence techniques. Compared to traditional statistical methods, ML methods have greater predictive effects. ML algorithms are widely applied in nearly all steps of model establishment, such as imaging feature extraction, predictive factor classification, and model development. Therefore, this review presents the literature pertaining to ML algorithms and aims to summarize the strengths and limitations of ML, as well as its potential value in prognostic prediction, after various treatment modalities for HCC.

US-Based Deep Learning Model for Differentiating Hepatocellular Carcinoma (HCC) From Other Malignancy in Cirrhotic Patients

“…Various machine learning algorithms consist of neural networks, support vector machines (SVM) and decision tree and random forest (RF) ( 21 ). However, due to the complexity of SVM and RF, the processing require more time for training the model compared with neural networks ( 21 ).…”

“…Various machine learning algorithms consist of neural networks, support vector machines (SVM) and decision tree and random forest (RF) ( 21 ). However, due to the complexity of SVM and RF, the processing require more time for training the model compared with neural networks ( 21 ). Inspired by human brain nature, convolution neural network (CNN) is the most popular DLM type in medical image analysis and can automatically identify and segment medical imaging ( 22 ).…”

Combined transarterial iodized oil injection and computed tomography-guided thermal ablation for hepatocellular carcinoma: utility of the iodized oil retention pattern

“…The present study failed to demonstrate a significant difference in OS between patients with complete Lipiodol retention and those with incomplete retention. The OS of HCC patients is related to numerous factors, including age, liver function, severity of the underlying liver disease, tumor burden, antiviral therapy, and performance status [24][25][26][27][28].…”

Predicting early refractoriness of transarterial chemoembolization in patients with hepatocellular carcinoma using a random forest algorithm: A pilot study

“…However, predictive models developed by traditional statistical methods are not reliable because the factors included in the models are too simple and utilize a low evidence level 28 . With the development of ML algorithm, more and more ML algorithm-based predictive models have been created 29 . Peng J et al 30 have established a convolutional neural network model and Abajian A et al 14 have created an RF model.…”