Application of Fuzzy Logic in Oral Cancer Risk Assessment
-
Ioana SCROBOTĂ
,
Grigore BĂCIUȚ
,
Adriana Gabriela FILIP
,
Bianca TODOR
,
Florin BLAGA
,
Mihaela Felicia BĂCIUȚ
Abstract
Background: The mapping of the malignization mechanism is still incomplete, but oxidative stress is strongly correlated to carcinogenesis. In our research, using fuzzy logic, we aimed to estimate the oxidative stress related-cancerization risk of the oral potentially malignant disorders.
Methods: Serum from 16 patients diagnosed (clinical and histopathological) with oral potentially malignant disorders (Dept. of Cranio-Maxillofacial Surgery and Radiology, ”Iuliu Hațieganu” University of Medicine and Pharmacy, Cluj Napoca, Romania) was processed fluorometric for malondialdehyde and proton donors assays (Dept. of Physiology,”Iuliu Hațieganu” University of Medicine and Pharmacy, Cluj-Napoca, Romania). The values were used as inputs, they were associated linguistic terms using MIN-MAX method and 25 IF-THEN inference rules were generated to estimate the output value, the cancerization risk appreciated on a scale from 1 to 10 - IF malondialdehyde is very high and donors protons are very low THEN the cancer risk is reaching the maximum value (Dept. of Industrial Engineering, Faculty of Managerial and Technological Engineering, University of Oradea, Oradea, Romania) (2012-2014).
Results: We estimated the cancerization risk of the oral potentially malignant disorders by implementing the multi-criteria decision support system based on serum malondialdehyde and proton donors' values. The risk was estimated as a concrete numerical value on a scale from 1 to 10 depending on the input numerical/linguistic value.
Conclusion: The multi-criteria decision support system proposed by us, integrated into a more complex computerized decision support system, could be used as an important aid in oral cancer screening and establish future medical decision in oral potentially malignant disorders.
Warnakulasuriya S (2009). Global epidemiology of oral and oropharyngeal cancer. Oral Oncol, 45 (4-5): 309–16.
Ferlay J, Soerjomataram I, Dikshit R, et al (2015). Cancer incidence and mortality worldwide: Sources, methods and major patterns in GLOBOCAN 2012. Int J Cancer, 136 (5): E359–86.
Llewellyn CD, Johnson NW, Warnakulasuriya KAAS (2001). Risk factors for squamous cell carcinoma of the oral cavity in young people-a comprehensive literature review. Oral Oncol, 37 (5): 401–18.
Lippman SM, Hong W.K (2001). Molecular Markers of the Risk of Oral Cancer. N Engl J Med, 344 (17): 1323-6.
Latha KC, Madhu B, Ayesha S, et al (2013). Visualization of risk in breast cancer using fuzzy logic in matlab environment. Int J Comput Intell Tech, 4 (1): 114-7.
Cruz JA, Wishart DS (2007). Applications of machine learning in cancer prediction and prognosis. Cancer Inform, 2: 59–77.
Doi K (2007). Computer-aided diagnosis in medical imaging: historical review, current status and future potential. Comput Med Imaging Graph, 31 (4-5): 198-211.
Kourou K, Exarchos TP, Exarchos KP, et al (2015). Machine learning applications in cancer prognosis and prediction. Comput Struct Biotechnol J, 13: 8–17.
Zadeh LA (1965). Fuzzy sets. Inf Control, 8(3): 338–53.
Zadeh LA (1996). Fuzzy Logic = Computing with Words. IEEE Trans Fuzzy Syst, 4 (2): 103-11.
Abbod MF, von Keyserlingk DG, et al (2001). Survey of utilisation of fuzzy technology in Medicine and Healthcare. Fuzzy Set Syst, 120 (2): 331–49.
Latha KC, Madhu B, Ayesha S, et al (2013). Visualization of risk in breast cancer using fuzzy logic in matlab environment. Int J Comput Intell Tech, 4 (1): 114-7.
Castellano G, Castellano C, Fanelli A (2005). Knowledge discovery by a neuro-fuzzy modeling framework. Fuzzy Set Syst, 149 (1): 187–207.
Zadeh LA (1975). The concept of linguistic variable and its applications to approximate reasoning-II. Inform Sci, 8 (4): 301–57.
Conti M, Morand PC, Levillain P, Lemonnier A (1991). Improved Fluorometric Determination of Malonaldehyde. Clin Chem, 37 (7): 1273-5.
Janaszewska A, Bartosz G (2002). Assay of total antioxidant capacity: comparison of four methods as applied to human blood plasma. Scand J Clin Lab Invest, 62 (3): 231-6.
Halliwell B (2007). Oxidative stress and cancer: have we moved forward? Biochem J, 401 (1): 1–11.
Scrobotă I, Mocan T, Filip A, et al (2010). Oxidants-antioxidants balance in oral lichen planus. Physiology, 20 (4): 28-31.
Scrobotă I, Mocan T, Cătoi C, et al (2011). Histopathological aspects and local implications of oxidative stress in patients with oral lichen planus. Rom J Morphol Embryol, 52 (4): 1305-9.
Scrobotă I, Alb C, Câlniceanu H, et al (2015). Assessment of serum lipid peroxidation and redox thiol system status in different stages of oral carcinogenesis. Rev Chim-Bucharest, 66 (9): 1467-70.
Choudhari SK, Chaudhary M, Gadbail AR, et al (2014). Oxidative and antioxidative mechanisms in oral cancer and precancer: a review. Oral Oncol, 50 (1): 10-8.
Klaunig JE, Kamendulis LM, Hocevar BA (2010). Oxidative stress and oxidative damage in carcinogenesis. Toxicol Pathol, 38 (1): 96-109.
Singh Z, Karthigesu Ip, Singh P, Kaur R (2014). Use of Malondialdehyde as a Biomarker for Assessing Oxidative Stress in Different Disease Pathologies: a Review. Iran J Public Health, 43 (3): 7-16.
Llesuy S, Evelson P, Campos AM, Lissi E (2001). Methodologies for evaluation of total antioxidant activities in complex mixtures. A critical review. Biol Res, 34 (2): 51–73.
Ross TJ (2010). Properties of Membership Functions, Fuzzification, and Defuzzification. In: Fuzzy Logic with Engineering Applications, Ed, John Wiley & Sons. Ltd, Chichester, UK, pp. 89-116.
Dombi J (1990). Membership function as an evaluation. Fuzzy Set Syst, 35 (1): 1-21.
Yilmaz A, Ayan K (2013). Cancer risk analysis by fuzzy logic approach and performance status of the model. Turk J Elec Eng Comp Sci, 21: 897–912.
Zheng W, Blot WJ, Diamond EL, et al (1993). Serum Micronutrients and the Subsequent Risk of Oral and Pharyngeal Cancer. Cancer Res, 53 (4): 795-8.
Salzman R, Pácal L, Tomandl J, et al (2009). Elevated malondialdehyde correlates with the extent of primary tumor and predicts poor prognosis of oropharyngeal cancer. Anticancer Res, 29 (10): 4227-31.
Korde SD, Basak A, Chaudhary M, et al (2011). Enhanced nitrosative and oxidative stress with decreased total antioxidant capacity in patients with oral precancer and oral squamous cell carcinoma. Oncology, 80 (5-6): 382-9.
Nagata T, Schmelzeisen R, Mattern D, et al (2005). Application of fuzzy inference to European patients to predict cervical lymph node metastasis in carcinoma of the tongue. Int J Oral Maxillofac Surg, 34 (2): 138–42.
Kareem SA [PhD Thesis]. Application of Artificial Neural Network for the Prognosis of Nasopharyngeal Carcinoma, University of Malaya, Malaya; 2002.
Wang CY, Lee TF, Fang CH, Chou JH (2012). Fuzzy logic-based prognostic score for outcome prediction in esophageal cancer. IEEE Trans Inf Technol Biomed, 16(6), 1224–30.
Rosma MD [PhD Thesis]. A fuzzy regression model for the prediction of oral cancer susceptibility. School of Electronics and Computer Science, University of Malaya, Malaya; 2009.
Hagerty RG, Butow PN, Ellis PM, et al (2005). Communicating prognosis in cancer care: a systematic review of the literature. Ann Oncol, 16 (7): 1005–53.
Scully C (2014). Challenges in predicting which oral mucosal potentially malignant disease will progress to neoplasia. Oral Dis, 20 (1): 1–5.
Reibel J (2003). Prognosis of Oral Pre-malignant Lesions: Significance of Clinical, Histopathological, and Molecular Biological Characteristics. Crit Rev Oral Biol Med, 14 (1): 47-62.
Holmstrup P, Vedtofte P, Reibel J, Stoltze K (2007). Oral premalignant lesions: is a biopsy reliable? J Oral Pathol Med, 36 (5): 262-6.
Poongodi M, Manjula L, Pradeepkumar S, Umadevi M (2012). Cancer prediction technique using fuzzy logic. Int J Curr Res, 4 (2): 106-110.
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| Issue | Vol 46 No 5 (2017) | |
| Section | Original Article(s) | |
| Keywords | ||
| Oral potentially malignant disorders Malondialdehyde Proton donors Cancer risk Fuzzy logic | ||
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