Machine learning methods for MRI biomarkers analysis of pediatric posterior fossa tumors

• Autorzy: Li Mengmeng , Shang Zhigang , Yang Zhongliang , Zhang Yong , Wan Hong

Identyfikatory

DOI

10.1016/j.bbe.2019.07.004

• Języki publikacji: EN

Abstrakty

EN

Medical imaging technologies provide an increasing number of opportunities for disease prediction and prognosis. Specifically, imaging biomarkers can quantify the entire tumor phenotypes to enhance the prediction. Machine learning technology can be explored to mine and analyze these biomarkers and to establish predictive models for the clinical applications. Several studies have applied various machine learning methods to imaging biomark-ers based clinical predictions of different diseases. Here we seek to evaluate different machine learning methods in pediatric posterior fossa tumor prediction. We present a machine learning based magnetic resonance imaging biomarkers analysis framework for two kinds of pediatric posterior fossa tumors. In details, three feature extraction methods are used to obtain 300 imaging biomarkers. 10 feature selection methods and 11 classifiers are evaluated by the quantified predictive performance and stability, and importance consistency of features and the influence of the experimental factors are also analyzed. Our results demonstrate that the CFS feature selection method (accuracy: 83.85 5.51%, stability: [0.84, 0.06]) and SVM classifier (accuracy: 85.38 3.47%, RSD: 4.77%) show relatively better performance than others and should be preferred. Among all the biomarkers, 17 texture features seem to be more important. Multifactor analysis results indicate the choice of classifier accounts for the most contribution to the variability in performance (37.25%). The machine learning based framework is efficient for pediatric posterior fossa tumors biomarkers analysis and could provide valuable references and decision support for assisted clinical diagnosis.

Słowa kluczowe

EN

pediatric posterior fossa tumor; magnetic resonance imaging; biomarker; machine learning; feature selection; SVM classifier

PL

rezonans magnetyczny; biomarker; uczenie maszynowe; selekcja cech

• Wydawca: Nałęcz Institute of Biocybernetics and Biomedical Engineering of the Polish Academy of Sciences ; Elsevier
• Czasopismo: Biocybernetics and Biomedical Engineering
• Rocznik: 2019
• Tom: Vol. 39, no. 3
• Strony: 765--774
• Opis fizyczny: Bibliogr. 37 poz., rys., tab., wykr.

Twórcy

autor

Li Mengmeng

• School of Electrical Engineering, Zhengzhou University, Zhengzhou, Henan, China; Industrial Technology Research Institute, Zhengzhou University, Zhengzhou, Henan, China

autor

Shang Zhigang

• School of Electrical Engineering, Zhengzhou University, Zhengzhou, Henan, China; Industrial Technology Research Institute, Zhengzhou University, Zhengzhou, Henan, China; Henan Key Laboratory of Brain Science and Brain-Computer Interface Technology, Zhengzhou, Henan, China

autor

Yang Zhongliang

• School of Electrical Engineering, Zhengzhou University, Zhengzhou, Henan, China; Industrial Technology Research Institute, Zhengzhou University, Zhengzhou, Henan, China

autor

Zhang Yong

• Magnetic Resonance Department, the first Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, China

autor

Wan Hong

• School of Electrical Engineering, Zhengzhou University, Zhengzhou, Henan, China; Industrial Technology Research Institute, Zhengzhou University, Zhengzhou, Henan, China; Henan Key Laboratory of Brain Science and Brain-Computer Interface Technology, Zhengzhou, Henan, China

Bibliografia

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Uwagi

PL

Opracowanie rekordu w ramach umowy 509/P-DUN/2018 ze środków MNiSW przeznaczonych na działalność upowszechniającą naukę (2019).