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Year : 2023  |  Volume : 12  |  Issue : 1  |  Page : 83

Predictive value of blood markers in nonfunctional pituitary adenomas using artificial neural network

1 Department of Neuroanesthesiology, Imam Hossein Hospital, Shahid Beheshti University of Medical Sciences, Tehran, Iran
2 Department of Neurosurgery, Imam Hossein Hospital, Shahid Beheshti University of Medical Sciences, Tehran, Iran
3 Department of Otorhinolaryngology, Imam Hossein Hospital, Shahid Beheshti University of Medical Sciences, Tehran, Iran
4 Department of Neurosurgery, Faculty of Neurosurgery, Jundishapur University of Medical Sciences, Ahwaz, Iran
5 Pharmacognosy Research Laboratories and Herbal Analysis Services, University of Greenwich, Central Avenue, Chatham-Maritime, UK
6 Clinical Research and Development Unit, Imam Hossein Hospital, Shahid Beheshti, University of Medical Sciences, Tehran, Iran

Correspondence Address:
Dr. Hamid Reza Khayat Kashani
Department of Neurosurgery, Imam Hossein Hospital, Madani Street, Tehran
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Source of Support: None, Conflict of Interest: None

DOI: 10.4103/abr.abr_183_21

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Background: Nonfunctioning pituitary adenomas (NFPAs) are the most common pituitary tumors and although they do not secrete hormones, they can have systemic effects. These tumors affect the function of other organs in the body by exerting pressure on the pituitary gland. There are differences between biomarkers NFPAs compared to healthy people. This study was conducted to show blood marker changes in adenomas compared to healthy people. Materials and Methods: This article compared the blood markers of NFPAs with healthy individuals retrospectively. The difference between blood markers in the two groups was statistically investigated where the predictive value of blood markers in the differentiation of the two groups was determined. An artificial neural network was also designed using the blood markers with its accuracy and predictive value determined. Results: A total of 96 NFPAs (nonfunctional pituitary adenoma) and 96 healthy individuals were evaluated. There was statistically a significant difference and positive correlation in platelet to lymphocyte ratio, neutrophil to lymphocyte ratio, and derived neutrophil to lymphocyte ratio between NFPAs and healthy individuals. There was a significant and negative correlation between red blood cell (RBC), lymphocyte, and monocyte between the two groups. RBC as an independent factor was associated with NFPAs. In this study, the artificial neural network was able to differentiate between NFPTs cases and healthy individuals with an accuracy of 81.2%. Conclusion: There are differences between blood markers in NFPAs relative to healthy people and the artificial neural network can accurately differentiate between them.

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