APA
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Leary, O. P., Zhong, Z., Bi, L., Jiao, Z., Dai, Y.-W., Ma, K., … Boxerman, J. L. (2024). MRI-Based Prediction of Clinical Improvement after Ventricular Shunt Placement for Normal Pressure Hydrocephalus: Development and Evaluation of an Integrated Multisequence Machine Learning Algorithm. American Journal of Neuroradiology.
Chicago/Turabian
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Leary, Owen P., Zhusi Zhong, Lulu Bi, Zhicheng Jiao, Yu-Wei Dai, Kevin Ma, Shanzeh Sayied, et al. “MRI-Based Prediction of Clinical Improvement after Ventricular Shunt Placement for Normal Pressure Hydrocephalus: Development and Evaluation of an Integrated Multisequence Machine Learning Algorithm.” American Journal of Neuroradiology (2024).
MLA
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Leary, Owen P., et al. “MRI-Based Prediction of Clinical Improvement after Ventricular Shunt Placement for Normal Pressure Hydrocephalus: Development and Evaluation of an Integrated Multisequence Machine Learning Algorithm.” American Journal of Neuroradiology, 2024.
BibTeX Click to copy
@article{owen2024a,
title = {MRI-Based Prediction of Clinical Improvement after Ventricular Shunt Placement for Normal Pressure Hydrocephalus: Development and Evaluation of an Integrated Multisequence Machine Learning Algorithm},
year = {2024},
journal = {American Journal of Neuroradiology},
author = {Leary, Owen P. and Zhong, Zhusi and Bi, Lulu and Jiao, Zhicheng and Dai, Yu-Wei and Ma, Kevin and Sayied, Shanzeh and Kargilis, Daniel and Imami, Maliha R. and Zhao, Lin-mei and Feng, Xue and Riccardello, Gerald and Collins, Scott and Svokos, K. and Moghekar, A. and Yang, Li and Bai, Harrison and Klinge, Petra M. and Boxerman, Jerrold L.}
}
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 postshunt NPH symptom improvement. MATERIALS AND METHODS: Patients with NPH who underwent MRI before shunt placement at a single center (2014–2021) were identified. Twelve-month postshunt improvement in mRS, incontinence, gait, and cognition were retrospectively abstracted from clinical documentation. 3D deep residual neural networks were built on skull-stripped T2-weighted and 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 data set 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 by using only 1 sequence, with area under the receiver operating characteristic (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 by using both T2-weighted and FLAIR sequences offered the best image-based prediction of postshunt symptom improvement, particularly for gait and overall function in terms of mRS.