Development of ensemble learning models for prognosis of hepatocellular carcinoma patients underwent postoperative adjuvant transarterial chemoembolization

Abstract

BackgroundPostoperative adjuvant transarterial chemoembolization (PA-TACE) has been increasing widely used to improve the prognosis of hepatocellular carcinoma (HCC) patients. However, clinical outcomes vary from patient to patient, which calls for individualized prognostic prediction and early management.MethodsA total of 274 HCC patients who underwent PA-TACE were enrolled in this study. The prediction performance of five machine learning models was compared and the prognostic variables of postoperative outcomes were identified.ResultsCompared with other machine learning models, the risk prediction model based on ensemble learning strategies, including Boosting, Bagging, and Stacking algorithms, presented better prediction performance for overall mortality and HCC recurrence. Moreover, the results showed that the Stacking algorithm had relatively low time consumption, good discriminative ability, and the best prediction performance. In addition, according to time-dependent ROC analysis, the ensemble learning strategies were found to perform well in predicting both OS and RFS for the patients. Our study also found that BCLC Stage, hsCRP/ALB and frequency of PA-TACE were relatively important variables in both overall mortality and recurrence, while MVI contributed more to the recurrence of the patients.ConclusionAmong the five machine learning models, the ensemble learning strategies, especially the Stacking algorithm, could better predict the prognosis of HCC patients following PA-TACE. Machine learning models could also help clinicians identify the important prognostic factors that are clinically useful in individualized patient monitoring and management.

Implications of rapid population growth on survey design and HIV estimates in the Rakai Community Cohort Study (RCCS), Uganda

“…To predict seroprevalence and viremia among residents in new households, an ensemble prediction model was built for each outcome. Ensemble models often employ bagging, boosting or stacking approaches to combine multiple machine learners, maximising performance of the prediction model when little is known about which single learner best predicts a given outcome 32. Super Learner—a stacking-type ensemble prediction model—has been used in a variety of health outcome prediction models and is generally robust to model misspecification 33–36.…”

“…Ensemble models often employ bagging, boosting or stacking approaches to combine multiple machine learners, maximising performance of the prediction model when little is known about which single learner best predicts a given outcome. 32 Super Learner-a stacking-type ensemble prediction modelhas been used in a variety of health outcome prediction models and is generally robust to model misspecification. [33][34][35][36] Performance and architecture of Super Learner has been described elsewhere.…”

Single-Cell RNA Sequencing Revealed That the Enrichment of TPI1+ Malignant Hepatocytes Was Linked to HCC Metastasis and Immunosuppressive Microenvironment

“… 5 , 6 Exploring possible factors affecting HCC metastasis and potential treatment options will effectively improve the prognosis of the patients. 5 , 7 As a typical inflammation-related process in tumorigenesis, immune evasion is an important feature of HCC occurrence and development. 8 Moreover, the tumor microenvironment (TME) in HCC has a strong dependence on the number and status of immune cells, which leads to poor clinical effectiveness of HCC treatment.…”