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Supervised Machine Learning Techniques For the Prediction of Hepatocellular Carcinoma Recurrence

Author(s): Andrea Mega, Luca Marzi, Alessandra Andreotti, Fabio Vittadello, Filippo Pelizzaro, Stefano Gitto, Gilbert Spizzo, Federica Ferro, Antonio Frena, Andreas Seeber

Background & Aims

Hepatocellular carcinoma (HCC) is the most frequent malignant tumor of the liver and its incidence is increasing worldwide. Several treatments are currently available, but predictors of cancer recurrence are poorly characterized. The development of artificial intelligence has recently made available a new tool called Machine Learning (ML). ML allows running strong prediction of several variables, after inputting several data into a dedicated software. This study aimed to create a MLmodel for predicting HCC recurrence.

Patients and methods

In this study, we analyzed retrospectively data of 166 patients who were managed at the Bolzano Regional Hospital between 1998 and 2019. In order to find the best predictive model, either both non-parametric and parametric models were evaluated. Non-parametric models trained in this study were the following: Random Forest (RF), Support Vector Machine (SVM) and K-Nearest Neighbours (KNN). Parametric model adopted was the logistic regression model with the elastic net algorithm (ENET).

Results

In our dataset, the Random Forest model is the most performant (AUC 0.712). Independently from the treatment performed, age at diagnosis, MELD, the absence of previous obesity, type of diagnosis, BMI, and BCLC emerged as significant HCC recurrence predictors.

Conclusion

ML may be a valuable tool in the prediction of HCC recurrence. Larger sample sizes are needed to create useful tool for the clinical management of patients with HCC.

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