Jie Dong1, Lei Li1, Shengxiang Liang2, Shujun Zhao1, Bin Zhang1, Yun Meng3, Yong Zhang3, Suxiao Li4. 1. School of Physics and Microelectronics, Zhengzhou University, NO.101 Kexue Road, Zhengzhou 450001, PR China. 2. National-Local Joint Engineering Research Center of Rehabilitation Medicine Technology, Fujian University of Traditional Chinese Medicine, Fuzhou, Fujian, PR China; Traditional Chinese Medicine Rehabilitation Research Center of State Administration of Traditional Chinese Medicine, Fujian University of Traditional Chinese Medicine, Fuzhou, Fujian, PR China. 3. Department of Magnetic Resonance, Zhengzhou University First Affiliated Hospital, Zhengzhou, Henan PR China. 4. School of Physics and Microelectronics, Zhengzhou University, NO.101 Kexue Road, Zhengzhou 450001, PR China. Electronic address: hnsuxiao@zzu.edu.cn.
Abstract
RATIONALE AND OBJECTIVES: Ependymoma (EP) and medulloblastoma (MB) of children are similar in age, location, manifestations and symptoms. Therefore, it is difficult to differentiate them through visual observation in clinical diagnosis. The aim of this study is to investigate the effectiveness of radiomics and machine-learning techniques on multimodal magnetic resonance imaging (MRI) in distinguish EP from MB. MATERIALS AND METHODS: Three dimensional (3D) tumors were semi-automatic segmented by radiologists from postcontrast T1-weighted images and apparent diffusion coefficient maps in 51 patients (24 EPs, 27 MBs). Then, we extracted radiomics features and further reduced them by three feature selection methods. For each feature selection method, 4 classifiers were adopted which yield 12 different models. After extensive crossvalidation, pairwise test were carried out in receiver operating characteristic curves to explore performance of these models. RESULTS: The radiomics model built with multivariable logistic regression as feature selection method and random forests as classifier had the best performance, area under the curve achieved 0.91 (95 % confidence interval 0.787-0.968). Five relevant features were highly correlated to discriminate EP and MB, which may used as imaging biomarkers to predict the kinds of tumors. CONCLUSION: The combination of radiomics and machine-learning approach on 3D multimodal MRI could well distinguish EP and MB of childhood, which assistant doctors in clinical diagnosis. Since there is no uniform model to obtained best performance for every specific data set, it is necessary to try different combination methods.
RATIONALE AND OBJECTIVES:Ependymoma (EP) and medulloblastoma (MB) of children are similar in age, location, manifestations and symptoms. Therefore, it is difficult to differentiate them through visual observation in clinical diagnosis. The aim of this study is to investigate the effectiveness of radiomics and machine-learning techniques on multimodal magnetic resonance imaging (MRI) in distinguish EP from MB. MATERIALS AND METHODS: Three dimensional (3D) tumors were semi-automatic segmented by radiologists from postcontrast T1-weighted images and apparent diffusion coefficient maps in 51 patients (24 EPs, 27 MBs). Then, we extracted radiomics features and further reduced them by three feature selection methods. For each feature selection method, 4 classifiers were adopted which yield 12 different models. After extensive crossvalidation, pairwise test were carried out in receiver operating characteristic curves to explore performance of these models. RESULTS: The radiomics model built with multivariable logistic regression as feature selection method and random forests as classifier had the best performance, area under the curve achieved 0.91 (95 % confidence interval 0.787-0.968). Five relevant features were highly correlated to discriminate EP and MB, which may used as imaging biomarkers to predict the kinds of tumors. CONCLUSION: The combination of radiomics and machine-learning approach on 3D multimodal MRI could well distinguish EP and MB of childhood, which assistant doctors in clinical diagnosis. Since there is no uniform model to obtained best performance for every specific data set, it is necessary to try different combination methods.
Authors: Siddhi Ramesh; Sukarn Chokkara; Timothy Shen; Ajay Major; Samuel L Volchenboum; Anoop Mayampurath; Mark A Applebaum Journal: JCO Clin Cancer Inform Date: 2021-12
Authors: M Zhang; L Tam; J Wright; M Mohammadzadeh; M Han; E Chen; M Wagner; J Nemalka; H Lai; A Eghbal; C Y Ho; R M Lober; S H Cheshier; N A Vitanza; G A Grant; L M Prolo; K W Yeom; A Jaju Journal: AJNR Am J Neuroradiol Date: 2022-03-31 Impact factor: 3.825
Authors: Michael Zhang; Samuel W Wong; Jason N Wright; Sebastian Toescu; Maryam Mohammadzadeh; Michelle Han; Seth Lummus; Matthias W Wagner; Derek Yecies; Hollie Lai; Azam Eghbal; Alireza Radmanesh; Jordan Nemelka; Stephen Harward; Michael Malinzak; Suzanne Laughlin; Sebastien Perreault; Kristina R M Braun; Arastoo Vossough; Tina Poussaint; Robert Goetti; Birgit Ertl-Wagner; Chang Y Ho; Ozgur Oztekin; Vijay Ramaswamy; Kshitij Mankad; Nicholas A Vitanza; Samuel H Cheshier; Mourad Said; Kristian Aquilina; Eric Thompson; Alok Jaju; Gerald A Grant; Robert M Lober; Kristen W Yeom Journal: Neurosurgery Date: 2021-10-13 Impact factor: 5.315
Authors: Michael Zhang; Edward Wang; Derek Yecies; Lydia T Tam; Michelle Han; Sebastian Toescu; Jason N Wright; Emre Altinmakas; Eric Chen; Alireza Radmanesh; Jordan Nemelka; Ozgur Oztekin; Matthias W Wagner; Robert M Lober; Birgit Ertl-Wagner; Chang Y Ho; Kshitij Mankad; Nicholas A Vitanza; Samuel H Cheshier; Tom S Jacques; Paul G Fisher; Kristian Aquilina; Mourad Said; Alok Jaju; Stefan Pfister; Michael D Taylor; Gerald A Grant; Sarah Mattonen; Vijay Ramaswamy; Kristen W Yeom Journal: Neuro Oncol Date: 2022-06-01 Impact factor: 13.029
Authors: M Zhang; S W Wong; S Lummus; M Han; A Radmanesh; S S Ahmadian; L M Prolo; H Lai; A Eghbal; O Oztekin; S H Cheshier; P G Fisher; C Y Ho; H Vogel; N A Vitanza; R M Lober; G A Grant; A Jaju; K W Yeom Journal: AJNR Am J Neuroradiol Date: 2021-07-15 Impact factor: 4.966