Establishing the relationship between the growth stage of a pituitary tumor and visual functions using the area of the zone of chiasmal pressure from the tumor
DOI:
https://doi.org/10.31288/Ukr.j.ophthalmol.202616370Keywords:
mathematical modeling, 3D modeling, pituitary adenoma, compressive optic neuropathy, optic chiasm, surface pressure area, optic neuropathyAbstract
Purpose: To develop a mathematical model of chiasmal compression using a computer model of the area of surface pressure from the tumor.
Material and Methods: We reviewed the medical records of 361 patients treated for compressive optic neuropathy due to pituitary adenoma (PA) at SI “Romodanov Neurosurgery Institute, NAMS of Ukraine” in 2018-2024. Patients underwent clinical neurological and ophthalmological examinations, magnetic resonance imaging (MRI), computed tomography (CT) and functional studies. Mathematical modeling was done in cooperation with the Department of Software Engineering in Energy Industry, National Technical University of Ukraine “Igor Sikorsky Kyiv Polytechnic Institute”. The study was based on modeling and statistical analysis.
Results: Patients (54.7% were women and 45.3% were men; mean age, 54.3 ± 12.5 years) were divided into 3 groups: group 1, early stage (small PAs, n = 115), group 2, moderately advanced stage (moderate-size PAs, n = 157), and group 3, advanced stage (giant PAs, n = 89). The difference between groups was statistically significant (p < 0.05). The study has confirmed the efficacy of using both CT and MRI for assessing tumor size. A mathematical model (z = 377.38 + 122.19x – 2.25y – 77.74x² – 2.91xy + 0,02y²) was developed for predicting the area of pressure zone from a PA. Critical thresholds of the area of the zone of chiasmal pressure from a PA were determined. An especially abrupt decline in retinal nerve fiber layer (RNFL) thickness was noted as pressure zone area (PZA) exceeded 200 conventional units (CU), and the worst visual acuity (VA; 0.42) and RNFL thickness (40 µm) values were noted for a PZA of 300 CU.
Conclusion: The model proposed has some substantial advantages compared to those developed previously. It is based on a complex approach that combines clinical data from various diagnostic modalities with mathematical modeling. This allows taking in account multiple parameters such as PZA, VA and RNFL thickness. High model fitness (R² = 0.9910) and clear correlations between parameters provide for a reliable prediction of the PZA. This has an important practical value for detecting the pathology and planning the treatment strategy early.
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