Clinical neurology and neurosurgery · 2024
Predictive model for orbital target volume for reconstruction in hyperostotic sphenoid wing meningiomas.
СтатьяНазвание и аннотация: Перевод в очереди
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Lasunin N V, Cherekaev V A, Danilov G V, Abdullaev A N, Efremov K V, Golbin D A, Melchenko S A
Аннотация
OBJECTIVE: To devise a predictive model for estimating the requisite volume of the orbit in patients poised for resection of hyperostotic spheno-orbital meningiomas.
MATERIAL AND METHODS: The predictive regression model was conceived through the retrospective analysis of perioperative radiological data from 25 patients who initially underwent surgery at the Burdenko Neurosurgery Center for hyperostotic spheno-orbital meningiomas grade I. The model quality metrics were evaluated utilizing the performance library in the R programming language, including the Akaike Information Criterion, Bayesian Information Criterion, adjusted R-squared, Root Mean Squared Error, and Sigma. An optimal model was discerned based on a comprehensive set of quality metrics.
RESULTS: An assessment of the linear relationship between the dynamics of the orbital volume ratio, the absolute and relative dimensions of the orbital volume and the positional alteration of the eyeglobes measured preoperatively was performed using linear regression. The best performance out of the 175 models tested showed a linear model that incorporated the preoperative orbital volume ratios, accounting for the tumor's soft tissue component, and the extent of proptosis. The established linear correlation between the globe's position alteration and the volume index dynamics was employed to predict the "optimal" orbital volume.
CONCLUSIONS: The developed predictive model suggests an "ideal" orbital volume based on data about the preoperative orbital volume ratios and the desired level of proptosis correction. A customized approach to repairing cranio-orbital bone defects in these patients is expected to consistently produce better functional and esthetic surgical results.
MATERIAL AND METHODS: The predictive regression model was conceived through the retrospective analysis of perioperative radiological data from 25 patients who initially underwent surgery at the Burdenko Neurosurgery Center for hyperostotic spheno-orbital meningiomas grade I. The model quality metrics were evaluated utilizing the performance library in the R programming language, including the Akaike Information Criterion, Bayesian Information Criterion, adjusted R-squared, Root Mean Squared Error, and Sigma. An optimal model was discerned based on a comprehensive set of quality metrics.
RESULTS: An assessment of the linear relationship between the dynamics of the orbital volume ratio, the absolute and relative dimensions of the orbital volume and the positional alteration of the eyeglobes measured preoperatively was performed using linear regression. The best performance out of the 175 models tested showed a linear model that incorporated the preoperative orbital volume ratios, accounting for the tumor's soft tissue component, and the extent of proptosis. The established linear correlation between the globe's position alteration and the volume index dynamics was employed to predict the "optimal" orbital volume.
CONCLUSIONS: The developed predictive model suggests an "ideal" orbital volume based on data about the preoperative orbital volume ratios and the desired level of proptosis correction. A customized approach to repairing cranio-orbital bone defects in these patients is expected to consistently produce better functional and esthetic surgical results.