Evidence map›Paper›PMID 37200767›Full record

ArticleAdvanced biomedical research2023

Predictive Value of Blood Markers in Nonfunctional Pituitary Adenomas using Artificial Neural Network.

Shahram Sayyadi, Hamid Reza Khayat Kashani, Rozita Jafari, Shirzad Azhari, Sohrab Salimi, Khalil Komlakh, Morteza Alesaadi, Pooyan Alizade, Habtemariam Solomon, Maryam Khayatkashani

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Article in Advanced biomedical research, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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2 · The registry

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

10 authors at 4 institutions in 2 countries.

Shahram SayyadiDepartment of Neuroanesthesiology, Imam Hossein Hospital, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
Hamid Reza Khayat KashaniDepartment of Neurosurgery, Imam Hossein Hospital, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
Rozita JafariDepartment of Otorhinolaryngology, Imam Hossein Hospital, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
Shirzad AzhariDepartment of Neurosurgery, Imam Hossein Hospital, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
Sohrab SalimiDepartment of Neuroanesthesiology, Imam Hossein Hospital, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
Khalil KomlakhDepartment of Neurosurgery, Imam Hossein Hospital, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
Morteza AlesaadiDepartment of Neurosurgery, Imam Hossein Hospital, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
Pooyan AlizadeDepartment of Neurosurgery, Faculty of Neurosurgery, Jundishapur University of Medical Sciences, Ahwaz, Iran.
Habtemariam SolomonPharmacognosy Research Laboratories and Herbal Analysis Services, University of Greenwich, Central Avenue, Chatham-Maritime, UK.
Maryam KhayatkashaniClinical Research and Development Unit, Imam Hossein Hospital, Shahid Beheshti, University of Medical Sciences, Tehran, Iran.
Shahid Beheshti University of Medical Sciences · IRImam Hossein Hospital · IRAhvaz Jundishapur University of Medical Sciences · IRUniversity of Greenwich · GB

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

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.

Indexed as

biomarkerserythrocyte countlymphocytesNeural networkneutrophilspituitary neoplasms

Identifiers

PMID37200767
PMCPMC10186052
OpenAlexW4377094001

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