MRI-Based Prediction of Clinical Improvement after Ventricular Shunt Placement for Normal Pressure Hydrocephalus: Development and Evaluation of an Integrated Multisequence Machine Learning Algorithm

作者：Owen P. Leary, Zhusi Zhong, Lulu Bi, Zhicheng Jiao, Yuwei Dai, Kevin Ma, Shanzeh Sayied, Daniel Kargilis, Maliha Imami, Linmei Zhao, Xue Feng, Gerald J Riccardello, Scott Collins, Konstantina Svokos, Abhay Moghekar, Li Yang, Harrison X. Bai, Petra M. Klinge, Jerrold L. Boxerman · 发表于：American Journal of Neuroradiology · 年份：2024 · DOI：10.3174/ajnr.a8372 · 被引用次数：7 · 研究领域：Cerebrospinal fluid and hydrocephalus、Fetal and Pediatric Neurological Disorders、Genetic and Kidney Cyst Diseases

ABSTRACT BACKGROUND AND PURPOSE: Symptoms of normal pressure hydrocephalus (NPH) are sometimes refractory to shunt placement, with limited ability to predict improvement for individual patients. We evaluated an MRI-based artificial intelligence method to predict post-shunt NPH symptom improvement. MATERIALS AND METHODS: NPH patients who underwent magnetic resonance imaging (MRI) prior to shunt placement at a single center (2014–2021) were identified. Twelve-month post-shunt improvement in modified Rankin Scale (mRS), incontinence, gait, and cognition were retrospectively abstracted from clinical documentation. 3D deep residual neural networks were built on skull stripped T2-weighted and fluid attenuated inversion recovery (FLAIR) images. Predictions based on both sequences were fused by additional network layers. Patients from 2014–2019 were used for parameter optimization, while those from 2020–2021 were used for testing. Models were validated on an external validation dataset from a second institution (n=33). RESULTS: Of 249 patients, n=201 and n=185 were included in the T2-based and FLAIR-based models according to imaging availability. The combination of T2-weighted and FLAIR sequences offered the best performance in mRS and gait improvement predictions relative to models trained on imaging acquired using only one sequence, with AUROC values of 0.7395 [0.5765–0.9024] for mRS and 0.8816 [0.8030–0.9602] for gait. For urinary incontinence and cognition, combined model ...