Publication: The development of a new prognostic logistic regression model: a case study on oral squamous cell carcinoma
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Date
2023-07
Authors
Azeem, Muhammad
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Abstract
Oral squamous cell carcinoma (OSCC) is the sixth most frequent cancer
worldwide. It is responsible for 80-90% of all mouth malignant neoplasms and has a
mortality rate of up to 50%. Oral cancer has multifactorial etiology, mainly smoking,
tobacco, alcohol consumption, betel quid chewing, and high-risk human
papillomavirus (HPV). A total of 57 patients were recruited from the clinic at the
Hospital Universiti Sains Malaysia (USM). In this retrospective study, advanced
computational statistical modeling techniques were used to evaluate data descriptions
on three risk factors studies: sociodemographic, clinicopathological, and surgical
margins features of OSCC patients. The R-Studio software and syntax were used to
design and develop the hybrid biometry method, implement, and the odd ratio. The
advanced approach was executed in three parts, such as developing syntax for R for
the biometry hybrid method which consists of data bootstrap methodology, multiple
layer feedforward neural network (MLFFNN), and logistic regression. Male gender,
smoking, betel quid, and alcohol habit variables were significantly related to death (p
< 0.05). Among clinicopathological features increasing tumour size, metastasis,
moderately and poorly differentiated OSCC, and Ki67 expression were significantly
related to deceased patients (p < 0.05). Furthermore, features of surgical margins
perineural invasion, bone invasion, and involvement of surgical margins were
significantly related to the death of OSCC patients (p < 0.05). This finding might
contribute to the underlying cause of poor prognosis. In conclusion, there exist potential risk factors in relation to OSCC in the Malaysian population. The conclusion
of the study might illustrate the superiority of the hybrid model technique used in the
study.