Extraordinary Potential of High Technologies Applications: A Literature Review and a Model of Assessment of Head and Neck Squamous Cell Carcinoma (HNSCC) Prognosis


Head and neck squamous cell carcinoma (HNSCC) is the sixth most common cause of cancer mortality in the world and the 5th most commonly occurring cancer (Siegel, R. 2014). In the last few decades a growing interest for the emerging data from both tumor biology and multimodality treatment in HNSCC has been developed. A huge number of new markers need to be managed with bio-informatics systems to elaborate and correlate clinical and molecular data. Data mining algorithms are a promising medical application. We used this technology to correlate blood samples with clinical outcome in 120 patients treated with chemoradiation for locally advanced HNSCC. Our results did not find a significant correlation because of the sample exiguity but they show the potential of this tool.

Share and Cite:

Camuto, C. and Denaro, N. (2014) Extraordinary Potential of High Technologies Applications: A Literature Review and a Model of Assessment of Head and Neck Squamous Cell Carcinoma (HNSCC) Prognosis. International Journal of Medical Physics, Clinical Engineering and Radiation Oncology, 3, 235-240. doi: 10.4236/ijmpcero.2014.34030.

Conflicts of Interest

The authors declare no conflicts of interest.


[1] Banville, D.L. (2009) Mining Chemical and Biological Information from the Drug Literature. Current Opinion in Drug Discovery & Development, 12, 376-387.
[2] Zhu, F., Patumcharoenpol, P., Zhang, C., Yang, Y., Chan, J., Meechai, A., Vongsangnak, W. and Shen, B. (2013) Biomedical Text Mining and Its Applications in Cancer Research. Journal of Biomedical Informatics, 46, 200-211.
[3] Siegel, R., Naishadham, D. and Jemal, A. (2013) Cancer statistics, 2013. CA: A Cancer Journal for Clinicians, 63, 11-30.
[4] Denaro, N., Russi, E.G., Adamo, V. and Merlano, M.C. (2014) State-of-the-Art and Emerging Treatment Options in the Management of Head and Neck Cancer: News from 2013. Oncology, 86, 212-229.
[5] Ang, K.K., Harris, J., Wheeler, R., Weber, R., Rosenthal, D.I., Nguyen-Tan, P.F., Westra, W.H., Chung, C.H., Jordan, R.C., Lu, C., Kim, H., Axelrod, R., Silverman, C.C., Redmond, K.P. and Gillison, M.L. (2010) Human Papillomavirus and Survival of Patients with Oropharyngeal Cancer. New England Journal of Medicine, 363, 24-35.
[6] Wu, J., Ho, C., Laskin, J., Gavin, D., Mak, P., Duncan, K., French, J., McGahan, C., Reid, S., Chia, S. and Cheung, H. (2013) The Development of a Standardized Software Platform to Support Provincial Population-Based Cancer Outcomes Units for Multiple Tumour Sites: OaSIS—Outcomes and Surveillance Integration System. Studies in Health Technology and Informatics, 183, 98-103.
[7] Naqa, I.E, Deasy, J.O., Mu, Y., Huang, E., Hope, A.J., Lindsay, P.E., Apte, A., Alaly, J. and Bradley, J.D. (2010) Datamining Approaches for Modeling Tumor Control Probability. Acta Oncologica, 49, 1363-73.
[8] Spencer, S.J., Bonnin, D.A., Deasy, J.O., Bradley, J.D. and El Naqa, I. (2009) Bioinformatics Methods for Learning Radiation-Induced Lung Inflammation from Heterogeneous Retrospective and Prospective Data. Journal of Biomedicine and Biotechnology, 2009, 1-14.

Copyright © 2021 by authors and Scientific Research Publishing Inc.

Creative Commons License

This work and the related PDF file are licensed under a Creative Commons Attribution 4.0 International License.