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

Owen P Leary; Zhusi Zhong; Lulu Bi; Zhicheng Jiao; Yu-Wei Dai; Kevin Ma; Shanzeh Sayied; Daniel Kargilis; Maliha Imami; Lin-Mei Zhao; Xue Feng; Gerald Riccardello; Scott Collins; Konstantina Svokos; Abhay Moghekar; Li Yang; Harrison Bai; Petra M Klinge; Jerrold L Boxerman

doi:10.3174/ajnr.A8372

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

AJNR Am J Neuroradiol. 2024 Jun 12:ajnr.A8372. doi: 10.3174/ajnr.A8372. Online ahead of print.

Authors

Owen P Leary¹, Zhusi Zhong¹, Lulu Bi¹, Zhicheng Jiao¹, Yu-Wei Dai¹, Kevin Ma¹, Shanzeh Sayied¹, Daniel Kargilis¹, Maliha Imami¹, Lin-Mei Zhao¹, Xue Feng¹, Gerald Riccardello¹, Scott Collins¹, Konstantina Svokos¹, Abhay Moghekar¹, Li Yang¹, Harrison Bai¹, Petra M Klinge¹, Jerrold L Boxerman¹

Affiliation

¹ From the Department of Neurosurgery (OPL, KM, SS, KS, PMK) and Department of Diagnostic Imaging (ZZ, ZJ, GR, SC, JLB), Warren Alpert Medical School of Brown University, Providence RI, USA; the Department of Electronic Engineering (ZZ), Xidian University, Xi'an, China; the Department of Neurology (YWD, LY), The Second Xiangya Hospital, Central South University, Hunan, China; the Department of Radiology (DK, MI, LMZ, HB) and Department of Neurology (AM), Johns Hopkins University School of Medicine, Baltimore MD, USA; Columbia University Vagelos College of Physicians and Surgeons, New York NY, USA (KM); Carina Medical, Lexington KY, USA (XF); and the Department of Biomedical Engineering (XF), University of Virginia, Charlottesville VA, USA.

PMID: 38866432
DOI: 10.3174/ajnr.A8372

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 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.