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. 2023 Mar 28:12:83.
doi: 10.4103/abr.abr_183_21. eCollection 2023.

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

Shahram Sayyadi  1 , Hamid Reza Khayat Kashani  2 , Rozita Jafari  3 , Shirzad Azhari  2 , Sohrab Salimi  1 , Khalil Komlakh  2 , Morteza Alesaadi  2 , Pooyan Alizade  4 , Habtemariam Solomon  5 , Maryam Khayatkashani  6
Affiliations

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

Shahram Sayyadi et al. Adv Biomed Res. .

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.

Keywords: Neural network; biomarkers; erythrocyte count; lymphocytes; neutrophils; pituitary neoplasms.

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Conflict of interest statement

There are no conflicts of interest.

Figures

Figure 1
Figure 1
Receiver operating characteristic curve demonstrates the predictive value of blood markers in differentiation between healthy and adenoma cases. The area under the curve of the markers are as follows: Neutrophil to lymphocyte ratio = 0.574, platelet to lymphocyte ratio = 0.653, derived neutrophil to lymphocyte = 0.577
Figure 2
Figure 2
Receiver operating characteristic curve demonstrates the predictive value of blood markers in differentiation between healthy and adenoma cases. The area under the curve of the markers are as follows: Red blood cell = 0.728, lymphocyte = 0.617, monocyte = 0.612
Figure 3
Figure 3
Confusion matrix of artificial neural network
Figure 4
Figure 4
Artificial neural network receiver operating characteristic curve. False positive rate; true positive rate
See this image and copyright information in PMC

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