Comput Methods Programs Biomed - Recurrence predictive models for patients with hepatocellular carcinoma after radiofrequency ablation using support vector machines with feature selection methods.

Tópicos

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Resumo

CKGROUND AND OBJECTIVE: Recurrence of hepatocellular carcinoma (HCC) is an important issue despite effective treatments with tumor eradication. Identification of patients who are at high risk for recurrence may provide more efficacious screening and detection of tumor recurrence. The aim of this study was to develop recurrence predictive models for HCC patients who received radiofrequency ablation (RFA) treatment.METHODS: From January 2007 to December 2009, 83 newly diagnosed HCC patients receiving RFA as their first treatment were enrolled. Five feature selection methods including genetic algorithm (GA), simulated annealing (SA) algorithm, random forests (RF) and hybrid methods (GA+RF and SA+RF) were utilized for selecting an important subset of features from a total of 16 clinical features. These feature selection methods were combined with support vector machine (SVM) for developing predictive models with better performance. Five-fold cross-validation was used to train and test SVM models.RESULTS: The developed SVM-based predictive models with hybrid feature selection methods and 5-fold cross-validation had averages of the sensitivity, specificity, accuracy, positive predictive value, negative predictive value, and area under the ROC curve as 67%, 86%, 82%, 69%, 90%, and 0.69, respectively.CONCLUSIONS: The SVM derived predictive model can provide suggestive high-risk recurrent patients, who should be closely followed up after complete RFA treatment.

Resumo Limpo

ckground object recurr hepatocellular carcinoma hcc import issu despit effect treatment tumor erad identif patient high risk recurr may provid efficaci screen detect tumor recurr aim studi develop recurr predict model hcc patient receiv radiofrequ ablat rfa treatmentmethod januari decemb newli diagnos hcc patient receiv rfa first treatment enrol five featur select method includ genet algorithm ga simul anneal sa algorithm random forest rf hybrid method garf sarf util select import subset featur total clinic featur featur select method combin support vector machin svm develop predict model better perform fivefold crossvalid use train test svm modelsresult develop svmbase predict model hybrid featur select method fold crossvalid averag sensit specif accuraci posit predict valu negat predict valu area roc curv respectivelyconclus svm deriv predict model can provid suggest highrisk recurr patient close follow complet rfa treatment

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