Osteosarcoma Patients Classification Using Plain X-Rays and Metabolomic Data

Rebecca Shen, Zhi Li, Linglin Zhang, Yingqi Hua, Min Mao, Zhicong Li, Zhengdong Cai, Yunping Qiu, Jonathan Gryak, Kayvan Najarian

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Osteosarcoma is the most common type of bone cancer. The primary means of osteosarcoma diagnosis is through evaluating plain x-rays. Using image analysis techniques, features that clinicians use to diagnose osteosarcoma can be quantified and studied using computer algorithms. In this paper, we classify benign tumor patients and osteosarcoma patients using both image features and metabolomic data. These two types of feature sets are processed with feature selection algorithms - recursive feature elimination and information gain. The selected features are then assessed by two classification models - random forest and support vector machine (SVM). The performances of the two models are evaluated and compared using receiver operating characteristic curves. The random forest classifier outperformed the SVM, with a sensitivity of 92 and a specificity of 78.

Original languageEnglish (US)
Title of host publication40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages690-693
Number of pages4
ISBN (Electronic)9781538636466
DOIs
StatePublished - Oct 26 2018
Event40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2018 - Honolulu, United States
Duration: Jul 18 2018Jul 21 2018

Publication series

NameProceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS
Volume2018-July
ISSN (Print)1557-170X

Other

Other40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2018
CountryUnited States
CityHonolulu
Period7/18/187/21/18

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Keywords

  • Cancer
  • Machine Learning
  • Osteosarcoma
  • Random Forest
  • SVM

ASJC Scopus subject areas

  • Signal Processing
  • Biomedical Engineering
  • Computer Vision and Pattern Recognition
  • Health Informatics

Cite this

Shen, R., Li, Z., Zhang, L., Hua, Y., Mao, M., Li, Z., Cai, Z., Qiu, Y., Gryak, J., & Najarian, K. (2018). Osteosarcoma Patients Classification Using Plain X-Rays and Metabolomic Data. In 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2018 (pp. 690-693). [8512338] (Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS; Vol. 2018-July). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/EMBC.2018.8512338