Article: Tackling imbalance radiomics in acoustic neuroma Journal: International Journal of Data Mining and Bioinformatics (IJDMB) 2019 Vol.22 No.4 pp.365 - 388 Abstract: Acoustic neuroma is a primary intracranial tumour of the myelin-forming cells of the 8th cranial nerve. Although it is a slow growing benign tumour, symptoms in the advanced phase can be serious. Hence, controlling tumour growth is essential and stereotactic radiosurgery, which can be performed with the CyberKnife robotic device, has proven effective for managing this disease. However, this approach may have side effects and a follow-up is necessary to assess its efficacy. To optimise the administration of this treatment, in this work we present a machine learning-based radiomics approach that first computes quantitative biomarkers from MR images routinely collected before the CyberKnife treatment and then predicts the treatment response. To tackle the challenge of class imbalance observed in the available dataset we present a cascade of cost-sensitive decision trees. We also experimentally compare the proposed approach with several approaches suited for learning under class skew. The results achieved demonstrate that radiomics has a great potential in predicting patients response to radiosurgery prior to the treatment that, in turns, can reflect into great advantages in therapy planning, sparing radiation toxicity and surgery when unnecessary. Inderscience Publishers - linking academia, business and industry through research

Title: Tackling imbalance radiomics in acoustic neuroma

Authors: Natascha Claudia D'Amico; Mario Merone; Rosa Sicilia; Ermanno Cordelli; Federico D'Antoni; Isa Bossi Zanetti; Giovanni Valbusa; Enzo Grossi; Giancarlo Beltramo; Deborah Fazzini; Giuseppe Scotti; Giulio Iannello; Paolo Soda

Addresses: Imaging Department, Centro Diagnostico Italiano S.p.A., Milan, Italy; Unit of Computer Systems and Bioinformatics, Università Campus Bio-Medico di Roma, Rome, Italy ' Unit of Computer Systems and Bioinformatics, Università Campus Bio-Medico di Roma, Rome, Italy; Joint Laboratory on Precision Medicine and BioData Analytics, Università Campus Bio-Medico di Roma - Centro Diagnostico Italiano S.p.A., Milan, Italy ' Unit of Computer Systems and Bioinformatics, Università Campus Bio-Medico di Roma, Rome, Italy; Joint Laboratory on Precision Medicine and BioData Analytics, Università Campus Bio-Medico di Roma - Centro Diagnostico Italiano S.p.A., Milan, Italy ' Unit of Computer Systems and Bioinformatics, Università Campus Bio-Medico di Roma, Rome, Italy; Joint Laboratory on Precision Medicine and BioData Analytics, Università Campus Bio-Medico di Roma - Centro Diagnostico Italiano S.p.A., Milan, Italy ' Unit of Computer Systems and Bioinformatics, Università Campus Bio-Medico di Roma, Rome, Italy; Joint Laboratory on Precision Medicine and BioData Analytics, Università Campus Bio-Medico di Roma - Centro Diagnostico Italiano S.p.A., Milan, Italy ' Cyberknife Department, Centro Diagnostico Italiano S.p.A., Milan, Italy; Joint Laboratory on Precision Medicine and BioData Analytics, Università Campus Bio-Medico di Roma - Centro Diagnostico Italiano S.p.A., Milan, Italy ' Imaging Department, Centro Diagnostico Italiano S.p.A., Milan, Italy; Joint Laboratory on Precision Medicine and BioData Analytics, Università Campus Bio-Medico di Roma - Centro Diagnostico Italiano S.p.A., Milan, Italy ' Bracco Imaging S.p.A. Milan, Italy ' Cyberknife Department, Centro Diagnostico Italiano S.p.A., Milan, Italy ' Imaging Department, Centro Diagnostico Italiano S.p.A., Milan, Italy; Joint Laboratory on Precision Medicine and BioData Analytics, Università Campus Bio-Medico di Roma - Centro Diagnostico Italiano S.p.A., Milan, Italy ' Imaging Department, Centro Diagnostico Italiano S.p.A., Milan, Italy; Joint Laboratory on Precision Medicine and BioData Analytics, Università Campus Bio-Medico di Roma - Centro Diagnostico Italiano S.p.A., Milan, Italy ' Unit of Computer Systems and Bioinformatics, Università Campus Bio-Medico di Roma, Rome, Italy; Joint Laboratory on Precision Medicine and BioData Analytics, Università Campus Bio-Medico di Roma - Centro Diagnostico Italiano S.p.A., Milan, Italy ' Unit of Computer Systems and Bioinformatics, Università Campus Bio-Medico di Roma, Rome, Italy; Joint Laboratory on Precision Medicine and BioData Analytics, Università Campus Bio-Medico di Roma - Centro Diagnostico Italiano S.p.A., Milan, Italy

Abstract: Acoustic neuroma is a primary intracranial tumour of the myelin-forming cells of the 8th cranial nerve. Although it is a slow growing benign tumour, symptoms in the advanced phase can be serious. Hence, controlling tumour growth is essential and stereotactic radiosurgery, which can be performed with the CyberKnife robotic device, has proven effective for managing this disease. However, this approach may have side effects and a follow-up is necessary to assess its efficacy. To optimise the administration of this treatment, in this work we present a machine learning-based radiomics approach that first computes quantitative biomarkers from MR images routinely collected before the CyberKnife treatment and then predicts the treatment response. To tackle the challenge of class imbalance observed in the available dataset we present a cascade of cost-sensitive decision trees. We also experimentally compare the proposed approach with several approaches suited for learning under class skew. The results achieved demonstrate that radiomics has a great potential in predicting patients response to radiosurgery prior to the treatment that, in turns, can reflect into great advantages in therapy planning, sparing radiation toxicity and surgery when unnecessary.

Keywords: radiomics; machine learning; imbalance learning; acoustic neuromas; CyberKnife.

DOI: 10.1504/IJDMB.2019.101396

International Journal of Data Mining and Bioinformatics, 2019 Vol.22 No.4, pp.365 - 388

Received: 07 Jun 2019
Accepted: 11 Jun 2019
Published online: 05 Aug 2019 *

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Title: Tackling imbalance radiomics in acoustic neuroma

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