Osteosarcoma Patients Classification Using Plain X-Rays and Metabolomic Data

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.

[1]  S. Mohamad R. Soroushmehr,et al.  Classifying osteosarcoma patients using machine learning approaches , 2017, 2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).

[2]  Igor Kononenko,et al.  Estimating Attributes: Analysis and Extensions of RELIEF , 1994, ECML.

[3]  Ronald L Eisenberg,et al.  Periosteal reaction. , 2009, AJR. American journal of roentgenology.

[4]  Mark A van de Wiel,et al.  Support Vector Machine Approach to Separate Control and Breast Cancer Serum Samples , 2008, Statistical applications in genetics and molecular biology.

[5]  Hong Tang,et al.  Data mining techniques for cancer detection using serum proteomic profiling , 2004, Artif. Intell. Medicine.

[6]  G. Ottaviani,et al.  The epidemiology of osteosarcoma. , 2009, Cancer treatment and research.

[7]  Xavier Robin,et al.  pROC: an open-source package for R and S+ to analyze and compare ROC curves , 2011, BMC Bioinformatics.

[8]  Jian Sun,et al.  Lazy snapping , 2004, SIGGRAPH 2004.

[9]  Jason Weston,et al.  Gene Selection for Cancer Classification using Support Vector Machines , 2002, Machine Learning.

[10]  C. Furlanello,et al.  Recursive feature elimination with random forest for PTR-MS analysis of agroindustrial products , 2006 .

[11]  Wei Sun,et al.  Serum and urinary metabonomic study of human osteosarcoma. , 2010, Journal of proteome research.

[12]  Zhang Hong,et al.  Texture feature extraction based on wavelet transform , 2010, 2010 International Conference on Computer Application and System Modeling (ICCASM 2010).