首页|Westlake University Reports Findings in Liver Cancer (Development of a machine l earning-based model to predict prognosis of alphafetoprotein-positive hepatocel lular carcinoma)
Westlake University Reports Findings in Liver Cancer (Development of a machine l earning-based model to predict prognosis of alphafetoprotein-positive hepatocel lular carcinoma)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-New research on Oncology-Liver Cance r is the subject of a report. According to news reporting originating in Hangzho u, People's Republic of China, by NewsRx journalists, research stated, "Patients with alpha-fetoprotein (AFP)-positive hepatocellular carcinoma (HCC) have aggre ssive biological behavior and poor prognosis. Therefore, survival time is one of the greatest concerns for patients with AFP-positive HCC." The news reporters obtained a quote from the research from Westlake University, "This study aimed to demonstrate the utilization of six machine learning (ML)-ba sed prognostic models to predict overall survival of patients with AFP-positive HCC. Data on patients with AFP-positive HCC were extracted from the Surveillance , Epidemiology, and End Results database. Six ML algorithms (extreme gradient bo osting [XGBoost], logistic regression [LR], support vector machine [SVM] , random forest [RF], K-nearest neighbor [KNN], and decision tree [ID3] ) were used to develop the prognostic models of patients with AFP-positive HCC a t one year, three years, and five years. Area under the receiver operating chara cteristic curve (AUC), confusion matrix, calibration curves, and decision curve analysis (DCA) were used to evaluate the model. A total of 2,038 patients with A FP-positive HCC were included for analysis. The 1-, 3-, and 5-year overall survi val rates were 60.7%, 28.9%, and 14.3%, r espectively. Seventeen features regarding demographics and clinicopathology were included in six ML algorithms to generate a prognostic model. The XGBoost model showed the best performance in predicting survival at 1-year (train set: AUC = 0.771; test set: AUC = 0.782), 3-year (train set: AUC = 0.763; test set: AUC = 0 .749) and 5-year (train set: AUC = 0.807; test set: AUC = 0.740). Furthermore, f or 1-, 3-, and 5-year survival prediction, the accuracy in the training and test sets was 0.709 and 0.726, 0.721 and 0.726, and 0.778 and 0.784 for the XGBoost model, respectively. Calibration curves and DCA exhibited good predictive perfor mance as well."
HangzhouPeople's Republic of ChinaAs iaBiological FactorsBiological Tumor MarkersCancerCarcinomasCyborgsE merging TechnologiesEpidemiologyFetal ProteinsHealth and MedicineLiver C ancerMachine LearningOncologyalpha-Fetoproteins
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