PREVAIL: Predicting Recovery through Estimation and Visualization of Active and Incident Lesions

Abstract

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.

Keywords: Lesion; MRI; Multiple sclerosis; Neuroimaging; Prediction.

Fig. 1

Representative predictions for the nT₁w model. Images are axial slices of lesions, with rows representing three example lesions with varying levels of predictive accuracy. For nT₁w intensities, red areas represent hyperintensity and blue areas represent hypointensity, with 0 (white) representing the intensity of normal-appearing white matter. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

Fig. 2

Representative predictions for the MTR model. Images are axial slices of lesions, with rows representing three example lesions with varying levels of predictive accuracy. For MTR, intensities range from 0 to 1, with a mean of approximately 0.5 for normal-appearing white matter.

Fig. 3

Scores for the six rater study questions, averaged across the three raters. The three rows show the distributions of the ratings of overall accuracy, accuracy of the degree of healing, and accuracy of the pattern of healing, respectively. Plots in the first column are distributions of ratings of the nT₁w prediction images, and plots in the second column are distributions of ratings of the MTR prediction images.