SVM Based Classification and Prediction System for Gastric Cancer Using Dominant Features of Saliva

Machine learning techniques are widely used for the diagnosis of cancers. In this study, we proposed a classification and prediction system for the diagnosis of gastric cancer based on saliva samples. Gastric cancer (GC) is classified into early gastric cancer (EGC) and advanced gastric cancer (AGC). The diagnosis of GC at an early stage will improve the survival rate. Computer-aided diagnostic (CAD) systems can assist the radiologists in the diagnosis of EGC. 220 saliva samples were collected from the non-cancerous and gastric cancerous persons and analyzed using high-performance liquid chromatography-mass spectrometry (HPLC-MS). Fourteen amino acid biomarkers were sufficient to distinguish the persons from malignant to benign and were observed in the saliva samples with dominant peaks. We used the support vector machine (SVM) for binary classification. The processed Raman dataset was used to train and test the developed model. SVM based neural networks were established using different kernels, which produced different results. Accuracy, specificity, sensitivity, and receiver operating characteristics (ROC) were used to evaluate the proposed classification model, along with mean average error (MAE), mean square Error (MSE), sum average error (SAE), and sum square error (SSE). We achieved an overall accuracy of 97.18%, specificity of 97.44%, and sensitivity of 96.88% for the proposed method. This established method owns the prospect of clinical translation.

[1]  K. Thangavel,et al.  Breathomics for Gastric Cancer Classification Using Back-propagation Neural Network , 2016, Journal of medical signals and sensors.

[2]  C. V. D. van de Velde,et al.  Gastric cancer: epidemiology, pathology and treatment. , 2003, Annals of oncology : official journal of the European Society for Medical Oncology.

[3]  Sercan Aksoy,et al.  Targeted therapies in gastric cancer and future perspectives. , 2016, World journal of gastroenterology.

[4]  G. G. Stokes.  "J." , 1890, The New Yale Book of Quotations.

[5]  A. Axon,et al.  Symptoms and diagnosis of gastric cancer at early curable stage. , 2006, Best practice & research. Clinical gastroenterology.

[6]  Nasser Hassan Sweilam,et al.  Support vector machine for diagnosis cancer disease: A comparative study , 2010 .

[7]  Daxiang Cui,et al.  Nanomaterial-based SERS sensing technology for biomedical application , 2019, Journal of Materials Chemistry B.

[8]  Xiao Zhi,et al.  EGFR Antibody Conjugated Bimetallic Au@Ag Nanorods for Enhanced SERS-Based Tumor Boundary Identification, Targeted Photoacoustic Imaging and Photothermal Therapy , 2016 .

[9]  Xiao Zhi,et al.  Identification of Volatile Biomarkers of Gastric Cancer Cells and Ultrasensitive Electrochemical Detection based on Sensing Interface of Au-Ag Alloy coated MWCNTs , 2014, Theranostics.

[10]  Lawrence Carin,et al.  Relevance vector machine feature selection and classification for underwater targets , 2003, Oceans 2003. Celebrating the Past ... Teaming Toward the Future (IEEE Cat. No.03CH37492).

[11]  Roopma Wadhwa,et al.  Gastric cancer—molecular and clinical dimensions , 2013, Nature Reviews Clinical Oncology.

[12]  Vladimir N. Vapnik,et al.  The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.

[13]  Hirofumi Nakayama,et al.  Molecular-pathological prognostic factors of gastric cancer: a review , 2005, Gastric Cancer.

[14]  U. Pastorino,et al.  Assessment of Circulating microRNAs in Plasma of Lung Cancer Patients , 2014, Molecules.

[15]  LinLin Shen,et al.  Deep learning based gastric cancer identification , 2018, 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018).

[16]  M. Tomita,et al.  Quantitative metabolome profiling of colon and stomach cancer microenvironment by capillary electrophoresis time-of-flight mass spectrometry. , 2009, Cancer research.

[17]  Daxiang Cui,et al.  Spontaneous implantation of gold nanoparticles on graphene oxide for salivary SERS sensing , 2019, Analytical Methods.

[18]  Huajian Gao,et al.  A microarray-based gastric carcinoma prewarning system. , 2005, World journal of gastroenterology.

[19]  Daxiang Cui,et al.  Salivary Analysis Based on Surface Enhanced Raman Scattering Sensors Distinguishes Early and Advanced Gastric Cancer Patients from Healthy Persons. , 2018, Journal of biomedical nanotechnology.

[20]  Margarita Osadchy,et al.  Deep Convolutional Neural Networks for Raman Spectrum Recognition: A Unified Solution , 2017, The Analyst.

[21]  Kan Wang,et al.  Breath Analysis Based on Surface-Enhanced Raman Scattering Sensors Distinguishes Early and Advanced Gastric Cancer Patients from Healthy Persons. , 2016, ACS nano.

[22]  Qingbo Li,et al.  Detection of Gastric Cancer with Fourier Transform Infrared Spectroscopy and Support Vector Machine Classification , 2013, BioMed research international.

[23]  J. Koenderink.  Q… , 2014, Les noms officiels des communes de Wallonie, de Bruxelles-Capitale et de la communaute germanophone.

[24]  S. Gokhale,et al.  Ultrasound characterization of breast masses , 2009, The Indian journal of radiology & imaging.

[25]  Yu Zheng,et al.  Simultaneous Quantitative Detection of Helicobacter Pylori Based on a Rapid and Sensitive Testing Platform using Quantum Dots-Labeled Immunochromatiographic Test Strips , 2016, Nanoscale Research Letters.

[26]  L. Tanoue.  Cancer Statistics, 2009 , 2010 .

[27]  Aaron Park,et al.  Baseline correction using asymmetrically reweighted penalized least squares smoothing. , 2015, The Analyst.

[28]  Harold J. Annegarn,et al.  Algorithm for fitting XRF, SEM and PIXE X-ray spectra backgrounds , 1996 .

[29]  Nello Cristianini,et al.  Support Vector Machines and Kernel Methods: The New Generation of Learning Machines , 2002, AI Mag..

[30]  Ki-Hyun Kim,et al.  A review of breath analysis for diagnosis of human health , 2012 .

[31]  David S. Wishart,et al.  Applications of Machine Learning in Cancer Prediction and Prognosis , 2006, Cancer informatics.

[32]  Tsuyoshi Murata,et al.  {m , 1934, ACML.

[33]  Stefan Michiels,et al.  Prediction of cancer outcome with microarrays: a multiple random validation strategy , 2005, The Lancet.

[34]  A. Mahadevan-Jansen,et al.  Automated Method for Subtraction of Fluorescence from Biological Raman Spectra , 2003, Applied spectroscopy.

[35]  Mehmet Engin,et al.  Early prostate cancer diagnosis by using artificial neural networks and support vector machines , 2009, Expert Syst. Appl..

[36]  H. Heneghan,et al.  MiRNAs as biomarkers and therapeutic targets in cancer. , 2010, Current opinion in pharmacology.

[37]  F. Otto,et al.  Gene deregulation in gastric cancer. , 2005, Gene.

[38]  Yongyi Yang,et al.  Computer-Aided Detection and Diagnosis of Breast Cancer With Mammography: Recent Advances , 2009, IEEE Transactions on Information Technology in Biomedicine.

[40]  Aixia Guo,et al.  Gene Selection for Cancer Classification using Support Vector Machines , 2014 .

[41]  B. Burwinkel,et al.  Cancer diagnosis and prognosis decoded by blood-based circulating microRNA signatures , 2013, Front. Genet..

[42]  S. Koscielny.  Why Most Gene Expression Signatures of Tumors Have Not Been Useful in the Clinic , 2010, Science Translational Medicine.

[43]  Yi-Zeng Liang,et al.  Baseline correction using adaptive iteratively reweighted penalized least squares. , 2010, The Analyst.

[44]  K. Zen,et al.  Circulating MicroRNAs: a novel class of biomarkers to diagnose and monitor human cancers , 2012, Medicinal research reviews.