A Convolutional Neural Network Application for Predicting the Locating of Squamous Cell Carcinoma in the Lung

Abstract

Squamous cell carcinoma, one of the most common types of lung cancer types, usually occurs in the middle, right or left bronchi. Squamous cell carcinoma can be easily detected by imaging methods to determine the location within the lung.  However, rarely the location of some tumor types cannot be determined. In this case, it may be delayed to obtain the results of the assay such as biopsy. This possible delay also means delayed diagnosis and delayed start of treatment. In order to solve this problem, it is possible to perform applications with machine learning methods. In this study, convolutional neural networks method was used to determine the location of cancerous tumor in squamous cell carcinoma of lung. With the designed convolutional neural network model, squamous cell carcinoma tumor location in lung cancer was estimated with an accuracy rate close to 100%.

Keywords

• Convolutional neuran networks,squamous cell carcinoma,classification,estimation,accuracy rate

References

1. Spiro S.G, Porter J.C. Lung cancer-Where are we today? Current advances in staging and nonsurgical treatment. American Journal of Respiratory and Critical Care Medicine, vol.166, no.9, pp.1166-1196, 2002.
2. World Health Organisation, The World Health Report, 2004.
3. Derman B.A., Mileham K.F., Bonomi P.D., Batus M., Fidler M.J. Treatment of advanced squamous cell carcinoma of the lung: A review, Transl Lung Cancer Res, vol.4, no.5, pp. 524-532, 2015.
4. Wusheng Y., Ignacio I. Wistuba, Michael R. Emmert-Buck, Heidi S. Erickson, Squamous cell carcinoma – similarities and differences among anatomical sites, Am J Cancer Res, vol.1, no.3, pp.275-300, 2011.
5. Wilkerson M.D., et al., Lung Squamous Cell Carcinoma mRNA Expression Subtypes Are Reproducible, Clinically Important, and Correspond to Normal Cell Types, Clinical Cancer Research. vol.16, no.19, 2010.
6. Ateş İ., et al., Squamous Cell Cancer of The Lung with Synchronous Renal Cell Carcinoma, Turkish Thoracic Journal, vol.17, no.3, pp.125-127, 2016.
7. Reck M. and Rabe K.F. Precision Diagnosis and Treatment for Advanced Non–Small-Cell Lung Cancer, The New England Journal of Medicine, vol.377, pp.849-861, 2017.
8. Schild S.E., et al. Long-term results of a phase III trial comparing once-daily radiotherapy with twice-daily radiotherapy in limited-stage small-cell lung cancer, International Journal of Radiation Oncology Biology- Physics, vol.59, no.4, pp.943-951, 2004.

9. Kulkarni A., Panditrao A. Classification of Lung Cancer Stages on CT Scan Images Using Image Processing, 2014 IEEE International Conference on Advanced Connnunication Control and Computing Teclmologies (lCACCCT).
10. Sarker P., et al. Segmentation and Classification of Lung Tumor from 3D CT Image using K-means Clustering Algorithm, Proceedings of the 4th International Conference on Advances in Electrical Engineering (ICAEE) 8-30 September, Dhaka, Bangladesh, 2017.
11. Usui S., et al., Differences in the prognostic implications of vascular invasion between lung adenocarcinoma and squamous cell carcinoma, An International Journal of for Lung Cancer and Other Thoracic Malignancies, vol.82, no.3, pp.407-412, 2013.
12. Vakili M., Yousefghahari B., Sharbatdaran M. Squamous cell carcinoma of lung with unusual site of metastasis, Caspian Journal of Internal Medicine, vol.3, no.2, pp.440-442, 2012.
13. Huang Z., Chen L.,Wang C. Classifying Lung Adenocarcinoma and Squamous Cell Carcinoma using RNA-Seq Data, Cancer Stud Mol Med Open Journal, vol.3, no.2, pp.27-31, 2017.
14. Pearce C. Convolutional Neural Networks and the Analysis of Cancer Imagery, Stanford University, 2017.
15. Fabio A. et al., Breast Cancer Histopathological Image Classification using Convolutional Neural Networks, Saint Etienne du Rouvray, France, 2017.
16. Pratt H., Coenen F., Broadbent D.M., Harding S.P., Zheng Y. Convolutional Neural Networks for Diabetic Retinopathy, International Conference On Medical Imaging Understanding and Analysis, MIUA 2016, 6-8 July 2016, Loughborough, UK.
17. Vallières, M. et al. Radiomics strategies for risk assessment of tumour failure in head-and-neck cancer, Scientific Report, vol.7, 2017.
18. http://www.cancerimagingarchive.net/ , date of access: 10 Jan 2018,