RT Journal Article
SR Electronic
T1 MRI-Based Prediction of Clinical Improvement Following Ventricular Shunt Placement for Normal Pressure Hydrocephalus (NPH): Development and Evaluation of an Integrated Multi-Sequence Machine Learning Algorithm
JF American Journal of Neuroradiology
JO Am. J. Neuroradiol.
FD American Society of Neuroradiology
SP ajnr.A8372
DO 10.3174/ajnr.A8372
A1 Leary, Owen P.
A1 Zhong, Zhusi
A1 Bi, Lulu
A1 Jiao, Zhicheng
A1 Dai, Yu-Wei
A1 Ma, Kevin
A1 Sayied, Shanzeh
A1 Kargilis, Daniel
A1 Imami, Maliha
A1 Zhao, Lin-Mei
A1 Feng, Xue
A1 Riccardello, Gerald
A1 Collins, Scott
A1 Svokos, Konstantina
A1 Moghekar, Abhay
A1 Yang, Li
A1 Bai, Harrison
A1 Klinge, Petra M.
A1 Boxerman, Jerrold L.
YR 2024
UL http://www.ajnr.org/content/early/2024/06/12/ajnr.A8372.abstract
AB 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 performances on predicting outcomes were similar to FLAIR-only performance, with AUROC values of 0.7874 [0.6845–0.8903] and 0.7230 [0.5600–0.8859].CONCLUSIONS: Application of a combined algorithm using both T2-weighted and FLAIR sequences offered the best image-based prediction of post-shunt symptom improvement, particularly for gait and overall function in terms of mRS.ABBREVIATIONS: NPH = normal pressure hydrocephalus; iNPH = idiopathic NPH; sNPH = secondary NPH; AI = artificial intelligence; ML = machine learning; CSF = cerebrospinal fluid; AUROC = area under the receiver operating characteristic; FLAIR = fluid attenuated inversion recovery; BMI = body mass index; CCI = Charlson Comorbidity Index; SD = standard deviation; IQR = interquartile range