Brain Ventricle Morphology Markers in Predicting Shunt Surgery Outcome in Idiopathic Normal-Pressure Hydrocephalus

Background Idiopathic normal pressure hydrocephalus (iNPH) is characterized by a clinical triad of symptoms: abnormal gait, memory problems, and urinary incontinence. Neuroimaging plays a crucial role in diagnosing iNPH. However, current radiolog- ical markers, though indicative, are not definitive, suggesting the limited capacity of these indices to capture mechanisms associated with iNPH and the reversibility of the symptoms. Aims This study aims to (1) determine the geometric features of the lateral ventricles, (2) develop a quantitative method for three-dimensional analysis, and (3) test the ability to predict response to shunt surgery. By examining these features as potential diagnostic markers, this research seeks to enhance the understanding of morphometric characteristics in iNPH, thereby paving the way for improved patient selection for surgical intervention. Methods Our study contained 170 patients (95 shunt responders and 75 non-responders) from the Kuopio NPH registry. Our inclusion criteria required pre-surgery and one-year post-surgery symptom assessments alongside preoperative anatomical magnetic resonance imaging (MRI). Volumetric brain segmentations were per- formed using cNeuro software on T1-MRI images, followed by the generation of 3D lateral ventricle meshes for geometric feature extraction. The classification task employed the LogitNet machine learning model to analyze 27 geometric fea- tures. Model performance evaluation utilized repeated nested cross-validation (10 rounds) with five inner folds for parameter tuning and five outer folds for model evaluation. Additionally, we generated a ranking of feature importance based on the LogitNet coefficients. Results Our analysis revealed that LogitNet achieved AUC = 0.660 (SD = 0.067) per- formance across 10 rounds of cross-validation in predicting the shunt surgery response. The most prominent feature contributing to the model’s prediction was asphericity. Conclusion Our analysis suggests that the proposed set of features, especially asphericity, effectively captures valuable information linked to the reversibility of iNPH.