Incremental learning for transductive support vector machine

Chen, Haiyan; Yu, Ying; Jia, Yizhen; Gu, Bin

doi:10.1016/j.patcog.2022.108982

Cited by 14 publications

(1 citation statement)

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“…The classifiers Adaptive boosting classifier (ADAC), bagging classifier (BAGC), decision tree classifier (DTC), K nearest neighbourhood classifier (KNNC), random forest classifier (RFC), support vector machine classifier (SVMC) and LogReg were implemented via MATLAB's Statistics and Machine Learning Toolbox. For Bernoulli Naïve Bayesian (BNB), stochastic gradient descent classifier (SGDC) and extreme gradient boosting classifier (XGBC), the publicly available MATLAB third‐party toolboxes were adapted to our requirements and integrated in our workflow 18–20 …”

Section: Methodsmentioning

confidence: 99%

Prognostic role of radiomics‐based body composition analysis for the 1‐year survival for hepatocellular carcinoma patients

Saalfeld,

Kreher,

Hille

et al. 2023

J cachexia sarcopenia muscle

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BackgroundParameters of body composition have prognostic potential in patients with oncologic diseases. The aim of the present study was to analyse the prognostic potential of radiomics‐based parameters of the skeletal musculature and adipose tissues in patients with advanced hepatocellular carcinoma (HCC).MethodsRadiomics features were extracted from a cohort of 297 HCC patients as post hoc sub‐study of the SORAMIC randomized controlled trial. Patients were treated with selective internal radiation therapy (SIRT) in combination with sorafenib or with sorafenib alone yielding two groups: (1) sorafenib monotherapy (n = 147) and (2) sorafenib and SIRT (n = 150). The main outcome was 1‐year survival. Segmentation of muscle tissue and adipose tissue was used to retrieve 881 features. Correlation analysis and feature cleansing yielded 292 features for each patient group and each tissue type. We combined 9 feature selection methods with 10 feature set compositions to build 90 feature sets. We used 11 classifiers to build 990 models. We subdivided the patient groups into a train and validation cohort and a test cohort, that is, one third of the patient groups.ResultsWe used the train and validation set to identify the best feature selection and classification model and applied it to the test set for each patient group. Classification yields for patients who underwent sorafenib monotherapy an accuracy of 75.51% and area under the curve (AUC) of 0.7576 (95% confidence interval [CI]: 0.6376–0.8776). For patients who underwent treatment with SIRT and sorafenib, results are accuracy = 78.00% and AUC = 0.8032 (95% CI: 0.6930–0.9134).ConclusionsParameters of radiomics‐based analysis of the skeletal musculature and adipose tissue predict 1‐year survival in patients with advanced HCC. The prognostic value of radiomics‐based parameters was higher in patients who were treated with SIRT and sorafenib.

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Section: Methodsmentioning

confidence: 99%