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Background and Study Aims: Prediction of prognosis and treatment outcomes for patients with hepatocellular carcinoma (HCC) is complex for most patients. Machine learning predictive analysis can be used to explore the rich information in electronic health records to discover hidden patterns and relationships. We aimed to develop a noninvasive algorithm for predicting outcome treatment options for patients with HCC., Patients and Methods: This cross-sectional study included 1298 patients with Hepatitis C virus-related HCC attending an HCC multidisciplinary clinic, Kasr Al-Aini Hospital, Cairo University, between 2009 and 2016. Using machine learning analysis, we constructed Reduced Error Pruning (REP) decision tree algorithms and applied Auto-WEKA to select the best classifier out of 39 algorithms., Results: The REP-tree algorithm predicted HCC management outcomes with a recall (sensitivity) of 0.658 and a precision (specificity) of 0.653 using only routine data. 854 (65.8%) instances were correctly identified, and 444 (34.2%) instances were incorrectly classified. Out of 31 attributes, liver decompensation was selected by REP-tree as the best predictor of HCC outcome (root node). With Auto-WEKA, the random subspace classifier was chosen as the best predictive algorithm with a recall (sensitivity) of 0.750 and a precision (specificity) of 0.75. There were 974 (75%) correctly classified instances and 324 (25%) incorrectly classified instances, which was better than REP-tree., Conclusion: Machine learning analysis explores data to discover hidden patterns and trends and enables the development of models to predict HCC treatment outcomes utilizing simple laboratory data. The random subspace classifier predicted the outcome more accurately than REP-tree., Competing Interests: Declaration of competing interests The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2022. Published by Elsevier B.V.)

Background: Conflicting studies were proposed either suggested or denied the relationship between early hepatocellular carcinoma (HCC) recurrence and the use of direct-acting antivirals (DAAs) for chronic hepatitis C management., Aim of the Study: To evaluate HCC recurrence rate post-DAAs and potential predictive factors.Study This prospective cohort study included all HCC patients achieved complete response attending our multidisciplinary HCC clinic, Cairo University, from November 2013 to February 2018. Group I (60 patients) who received DAAs after HCC ablation and group II (273 patients) who were DAAs-untreated. We studied factors that could play a role in HCC recurrence., Results: The sustained virological response rate was 88.3% among DAA-treated patients. HCC recurrence rate was 45% in the post-DAA group vs. 19% in the non-DAAs group; P < 0.001. Mean survival was significantly higher in the post-DAA group (34.23 ± 16.16 vs. 23.92 ± 13.99 months respectively; P value <0.001). There was a significant correlation between HCC recurrence rate and age, male gender, mean size of tumors and time interval between complete HCC ablation and occurrence of HCC recurrence., Conclusion: Our study reports high rate of HCC recurrence post-DAA therapy in patients treated with transarterial chemoembolization but not in those treated with curative measures. DAA therapy after curative treatment for HCC led to significantly earlier HCC recurrence, which correlated with specific clinic-pathologic features in our prospective single-institution study. However, future independent prospective randomized studies are warranted to evaluate this correlation which may lead to a change in the current standard-of-care approach to patients with hepatitis C virus-related HCC., (Copyright © 2020 Wolters Kluwer Health, Inc. All rights reserved.)