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Krishnan AP, Song Z, Clayton D, et al. Joint MRI T1 Unenhancing and Contrast-enhancing Multiple Sclerosis Lesion Segmentation with Deep Learning in OPERA Trials. Radiology. 2021;211528doi: 10.1148/radiol.211528.
Krishnan, A. P., Song, Z., Clayton, D., Gaetano, L., Jia, X., de Crespigny, A., Bengtsson, T., & Carano, R. A. D. (2021). Joint MRI T1 Unenhancing and Contrast-enhancing Multiple Sclerosis Lesion Segmentation with Deep Learning in OPERA Trials. Radiology, 211528. https://doi.org/10.1148/radiol.211528
Krishnan, Anitha Priya, et al. "Joint MRI T1 Unenhancing and Contrast-enhancing Multiple Sclerosis Lesion Segmentation with Deep Learning in OPERA Trials." Radiology vol. (2021): 211528. doi: https://doi.org/10.1148/radiol.211528
Krishnan AP, Song Z, Clayton D, Gaetano L, Jia X, de Crespigny A, Bengtsson T, Carano RAD. Joint MRI T1 Unenhancing and Contrast-enhancing Multiple Sclerosis Lesion Segmentation with Deep Learning in OPERA Trials. Radiology. 2021 Dec 14;211528. doi: 10.1148/radiol.211528. Epub 2021 Dec 14. PMID: 34904871.
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Radiology. 2021 Dec 14;211528. doi: 10.1148/radiol.211528. Epub 2021 Dec 14.

Joint MRI T1 Unenhancing and Contrast-enhancing Multiple Sclerosis Lesion Segmentation with Deep Learning in OPERA Trials.

Radiology

Anitha Priya Krishnan, Zhuang Song, David Clayton, Laura Gaetano, Xiaoming Jia, Alex de Crespigny, Thomas Bengtsson, Richard A D Carano

Affiliations

  1. From the Department of Product Development-Personalized HealthCare Imaging (A.P.K., Z.S., T.B., R.A.D.C.), Clinical Imaging Group, gRED (D.C., A.d.C.), and DevSci OMNI-Biomarker Development (X.J.), Genentech, 600 E Grand Ave, South San Francisco, CA 94080; and Global Product Development Medical Affairs, Neuroscience, F. Hoffmann-La Roche, Basel, Switzerland (L.G.).

PMID: 34904871 DOI: 10.1148/radiol.211528

Abstract

Background Deep learning-based segmentation could facilitate rapid and reproducible T1 lesion load assessments, which is crucial for disease management in multiple sclerosis (MS). T1 unenhancing and contrast-enhancing lesions in MS are those that enhance or do not enhance after administration of a gadolinium-based contrast agent at T1-weighted MRI. Purpose To develop deep learning models for automated assessment of T1 unenhancing and contrast-enhancing lesions; to investigate if joint training improved performance; to reproduce a known ocrelizumab treatment response; and to evaluate the association of baseline T1-weighted imaging metrics with clinical outcomes in relapsing MS clinical trials. Materials and Methods Joint and individual deep learning models (U-Nets) were developed retrospectively on multimodal MRI data sets from large multicenter OPERA trials of relapsing MS (August 2011 to May 2015). The joint model included cross-network connections and a combined loss function. Models were trained on OPERA I data sets with three-fold cross-validation. OPERA II data sets were the internal test set. Dice coefficients, lesion true-positive and false-positive rates, and areas under the receiver operating characteristic curve (AUCs) were used to evaluate model performance. Association of baseline imaging metrics with clinical outcomes was assessed with Cox proportional hazards models. Results A total of 796 patients (3030 visits; mean age, 37 years ± 9; 521 women) from the OPERA II trial were evaluated. The joint model achieved a mean Dice coefficient of 0.77 and 0.74, lesion true-positive rate of 0.88 and 0.86, and lesion false-positive rate of 0.04 and 0.19 for T1 contrast-enhancing and T1 unenhancing lesion segmentation, respectively. Joint training improved performance for smaller T1 contrast-enhancing lesions (≤0.06 mL; individual training AUC: 0.75; joint training AUC: 0.87;

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