@inproceedings{6e523d939c4b4b54a8a1e939e2c0b273,
title = "Osteosarcoma Patients Classification Using Plain X-Rays and Metabolomic Data",
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.",
keywords = "Cancer, Machine Learning, Osteosarcoma, Random Forest, SVM",
author = "Rebecca Shen and Zhi Li and Linglin Zhang and Yingqi Hua and Min Mao and Zhicong Li and Zhengdong Cai and Yunping Qiu and Jonathan Gryak and Kayvan Najarian",
note = "Publisher Copyright: {\textcopyright} 2018 IEEE.; 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2018 ; Conference date: 18-07-2018 Through 21-07-2018",
year = "2018",
month = oct,
day = "26",
doi = "10.1109/EMBC.2018.8512338",
language = "English (US)",
series = "Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "690--693",
booktitle = "40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2018",
}
