TY - JOUR
T1 - PREVAIL
T2 - Predicting Recovery through Estimation and Visualization of Active and Incident Lesions
AU - Dworkin, Jordan D.
AU - Sweeney, Elizabeth M.
AU - Schindler, Matthew K.
AU - Chahin, Salim
AU - Reich, Daniel S.
AU - Shinohara, Russell T.
N1 - Funding Information:
The authors would like to thank Blake Dewey, the Neuroimmunology Branch clinical group and the technicians at the NIH who were instrumental in helping to collect and process the study data. The project described is supported in part by the NIH grants RO1 NS085211 and R21 NS093349 from the National Institute of Neurological Disorders and Stroke (NINDS). The research is also supported by the Intramural Research Program of NINDS . The content is solely the responsibility of the authors and does not necessarily represent the official views of the funding agencies.
Publisher Copyright:
© 2016 The Authors
PY - 2016
Y1 - 2016
N2 - Objective The goal of this study was to develop a model that integrates imaging and clinical information observed at lesion incidence for predicting the recovery of white matter lesions in multiple sclerosis (MS) patients. Methods Demographic, clinical, and magnetic resonance imaging (MRI) data were obtained from 60 subjects with MS as part of a natural history study at the National Institute of Neurological Disorders and Stroke. A total of 401 lesions met the inclusion criteria and were used in the study. Imaging features were extracted from the intensity-normalized T1-weighted (T1w) and T2-weighted sequences as well as magnetization transfer ratio (MTR) sequence acquired at lesion incidence. T1w and MTR signatures were also extracted from images acquired one-year post-incidence. Imaging features were integrated with clinical and demographic data observed at lesion incidence to create statistical prediction models for long-term damage within the lesion. Validation The performance of the T1w and MTR predictions was assessed in two ways: first, the predictive accuracy was measured quantitatively using leave-one-lesion-out cross-validated (CV) mean-squared predictive error. Then, to assess the prediction performance from the perspective of expert clinicians, three board-certified MS clinicians were asked to individually score how similar the CV model-predicted one-year appearance was to the true one-year appearance for a random sample of 100 lesions. Results The cross-validated root-mean-square predictive error was 0.95 for normalized T1w and 0.064 for MTR, compared to the estimated measurement errors of 0.48 and 0.078 respectively. The three expert raters agreed that T1w and MTR predictions closely resembled the true one-year follow-up appearance of the lesions in both degree and pattern of recovery within lesions. Conclusion This study demonstrates that by using only information from a single visit at incidence, we can predict how a new lesion will recover using relatively simple statistical techniques. The potential to visualize the likely course of recovery has implications for clinical decision-making, as well as trial enrichment.
AB - Objective The goal of this study was to develop a model that integrates imaging and clinical information observed at lesion incidence for predicting the recovery of white matter lesions in multiple sclerosis (MS) patients. Methods Demographic, clinical, and magnetic resonance imaging (MRI) data were obtained from 60 subjects with MS as part of a natural history study at the National Institute of Neurological Disorders and Stroke. A total of 401 lesions met the inclusion criteria and were used in the study. Imaging features were extracted from the intensity-normalized T1-weighted (T1w) and T2-weighted sequences as well as magnetization transfer ratio (MTR) sequence acquired at lesion incidence. T1w and MTR signatures were also extracted from images acquired one-year post-incidence. Imaging features were integrated with clinical and demographic data observed at lesion incidence to create statistical prediction models for long-term damage within the lesion. Validation The performance of the T1w and MTR predictions was assessed in two ways: first, the predictive accuracy was measured quantitatively using leave-one-lesion-out cross-validated (CV) mean-squared predictive error. Then, to assess the prediction performance from the perspective of expert clinicians, three board-certified MS clinicians were asked to individually score how similar the CV model-predicted one-year appearance was to the true one-year appearance for a random sample of 100 lesions. Results The cross-validated root-mean-square predictive error was 0.95 for normalized T1w and 0.064 for MTR, compared to the estimated measurement errors of 0.48 and 0.078 respectively. The three expert raters agreed that T1w and MTR predictions closely resembled the true one-year follow-up appearance of the lesions in both degree and pattern of recovery within lesions. Conclusion This study demonstrates that by using only information from a single visit at incidence, we can predict how a new lesion will recover using relatively simple statistical techniques. The potential to visualize the likely course of recovery has implications for clinical decision-making, as well as trial enrichment.
KW - Lesion
KW - MRI
KW - Multiple sclerosis
KW - Neuroimaging
KW - Prediction
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U2 - 10.1016/j.nicl.2016.07.015
DO - 10.1016/j.nicl.2016.07.015
M3 - Article
C2 - 27551666
AN - SCOPUS:84982914633
SN - 2213-1582
VL - 12
SP - 293
EP - 299
JO - NeuroImage: Clinical
JF - NeuroImage: Clinical
ER -