Author Of 3 Presentations
P0006 - Detecting treatment response on T1 gadolinium enhancing lesion burden in clinical trials of multiple sclerosis with deep learning (ID 998)
Abstract
Background
Ocrelizumab (OCR) is a humanized anti-CD20+ monoclonal antibody approved for the treatment of relapsing and primary progressive forms of multiple sclerosis (MS). It suppresses the development of new areas of inflammation as shown by the reduction in the number of T1 gadolinium (Gd) enhancing lesions. Deep learning (DL) based segmentation of lesions has the potential to automate these manual reads; enabling faster and more reproducible quantification.
Objectives
To evaluate a DL model’s ability to detect OCR treatment response on the number of T1 Gd enhancing lesions in clinical trials of relapsing remitting MS (RRMS).
Methods
We used images from Opera I trials (NCT01247324, n=898), to train a Unet model to segment both unenhancing and Gd-enhancing T1 lesions. T1 Gd- enhancing lesions in ground truth (GT) masks were present in ~44% of patients at baseline and ~15% across all time points. We created a dataset with an equal number of imaging volumes with and without enhancing lesions and used 70%-30% data splits for training and validation. The model was tested on images from Opera II trials (NCT01412333, n=905). To detect significant differences between treatment and control arms, we performed a negative binomial regression on the number of T1 Gd-enhancing lesions with baseline imaging and clinical covariates.
Results
The DL model achieved a mean dice coefficient (DC) of 0.72, lesion true positive rate of 0.92 (LTPR), lesion false positive rate (LFPR) of 0.06 and a volume correlation coefficient (CC) of 0.97 for Gd-enhancing lesion segmentation. For unenhancing lesion segmentation, the mean DC was 0.68, LTPR 0.78, LFPR 0.18 and volume CC of 0.97. The model had the highest false positives for lesions smaller than 10 voxels (voxel size: 1x1x3 mm3). For Gd-enhancing lesion segmentation a significant OCR treatment effect (p<0.001) in reducing the mean number of Gd-enhancing lesions at 24, 48 and 96 weeks (92%, 96%, 97% reduction from GT manual reads vs 67%, 71%, 78% from model predictions) was detected.
Conclusions
Our DL model performed Gd-enhancing lesion segmentation comparably to similar DL models in the literature and showed a high correlation to GT manual reads. Our model also had sufficiently high sensitivity to detect an OCR treatment response consistent with neuro-radiologist assessments. To our knowledge, this is the first study to report that a DL model has the sensitivity to detect treatment response on Gd-enhancing lesions.
P0007 - Detecting treatment response on T2 lesion burden in multiple sclerosis clinical trials with deep learning (ID 1171)
Abstract
Background
Deep Learning (DL) methods are promising to automate or assist the radiologist in many of the measurements currently made manually during clinical trial drug development; thereby reducing inter/intra rater variability and reading time. T2 lesion burden is a routine imaging endpoint in Multiple Sclerosis (MS) clinical trials. Ocrelizumab (OCR) is a humanized anti-CD20+ monoclonal antibody approved for the treatment of relapsing and primary progressive forms of MS. It has been shown to strongly suppress the number of new and enlarging T2 lesions (compared to control treatment) in the relapsing remitting MS (RRMS) based on manual neuro-radiologist assessments.
Objectives
Evaluate DL models to quantify T2 lesion burden and detect the therapeutic response identified by manual neuro-radiologist assessments.
Methods
We evaluated three DL models using multimodal brain MRI (T2w, FLAIR and T1w; voxel size of 1x1x3 mm3): a stack model with three consecutive slices as input; a patch model with 3D sub volumes as input; and a novel model architecture. Models were trained on Opera I (NCT01247324, n=898) datasets (baseline) and tested on Opera II (NCT01412333, n=905) datasets (baseline, 24, 48 and 96 weeks). Number of new and enlarging T2 lesions were estimated heuristically from serial lesion masks predicted by the model.
Results
Lesion size distributions were heavily skewed toward small lesions (~40% < 10 voxels, ~80% < 50 voxels with a minimum lesion size threshold of 3 voxels). All models achieved similar mean dice coefficient (0.7), mean lesion true positive rate (LTPR) of 0.87, 0.85, 0.82 and mean lesion false positive rate (LFPR) of 0.23, 0.24, 0.18. Generally, the models overestimated the number of new and enlarging T2 lesions when compared to the ground truth (GT) masks. This was driven by a high number of false positives (FP) for small lesions. All three models were able to detect a significant treatment response in favor of OCR at weeks 48 (p<0.01) and 96 (p<0.001). Only the novel model showed a significant treatment effect at week 24 (p<0.01).
Conclusions
The cross-sectional performance metrics of all 3 models were comparable to those reported in literature. All models successfully reproduced the treatment response from GT manual reads at 48 and 96 weeks, but only the novel model detected the early response at 24 weeks seen in the GT assessments. To further improve the model’s reliability, future work will be aimed to reduce the number of FPs for small size lesions.
P0613 - Ocrelizumab Reduces Regional Brain Atrophy in Relapsing Multiple Sclerosis as Assessed by Longitudinal Deformation-based Morphometry (ID 1228)
Abstract
Background
Ocrelizumab (OCR) is a humanised anti-CD20+ monoclonal antibody approved for the treatment of relapsing and primary progressive forms of multiple sclerosis (MS). It has been reported that OCR reduces brain atrophy in MS patients, however, a voxel-based approach has never been reported.
Objectives
To identify brain regions where atrophy is modified by the OCR treatment in Relapsing MS (RMS), using a fully automatic, voxel-based approach.
Methods
We applied longitudinal deformation-based morphometry (L-DBM) using the Advanced Normalization Tools (ANTs) to T1-weighted 3D MPRAGE brain MRI (voxel=1x1x3 mm3 ) from two clinical trials of OCR for RMS (Opera I: NCT01247324 and Opera II: NCT01412333). For each MRI visit, a Jacobian map was derived based on the deformation generated by registering the scan to a Single-Subject Template (SST) constructed for the same patient. Jacobian maps in patient SSTs were mapped to a population brain template for group analysis. Patients were included if MRI data were available for all visits (baseline, Weeks 24, 48, and 96) and the images were of sufficient quality to be successfully processed through the L-DBM pipeline (683 in the OCR arm, 580 in the active control arm of Interferon beta-1a). Voxelwise ANOVA was iterated over the whole brain to detect an interaction between Treatment and Time (p<0.01 with familywise error correction). Post-hoc analysis was done with mean Jacobian of identified regions, including Linear Mixed Effect (LME) analysis.
Results
Voxelwise ANOVA identified one cluster of voxels in the brain (besides the ventricles) that were contained within parts of thalamus, brainstem, and cerebellum. From the analysis of mean Jacobian of this cluster region, the percent volume reduction from baseline was significantly smaller in the treatment arm than the control arm at every follow-up visit (p<0.0001, t>8.0, relative difference>75%). LME analysis of the mean Jacobian of the cluster region confirmed that there was a strong interaction between Treatment and Time (F=18.35), for which age and sex were controlled.
Conclusions
L-DBM has identified several brain structures where atrophy was modified by the OCR treatment in RMS. The L-DBM analysis was performed at the voxel level and these regional findings are consistent with previous reports. Localization of the treatment-responsive brain structures may have an implication for future assessment of clinical outcomes in MS.
Presenter Of 1 Presentation
P0613 - Ocrelizumab Reduces Regional Brain Atrophy in Relapsing Multiple Sclerosis as Assessed by Longitudinal Deformation-based Morphometry (ID 1228)
Abstract
Background
Ocrelizumab (OCR) is a humanised anti-CD20+ monoclonal antibody approved for the treatment of relapsing and primary progressive forms of multiple sclerosis (MS). It has been reported that OCR reduces brain atrophy in MS patients, however, a voxel-based approach has never been reported.
Objectives
To identify brain regions where atrophy is modified by the OCR treatment in Relapsing MS (RMS), using a fully automatic, voxel-based approach.
Methods
We applied longitudinal deformation-based morphometry (L-DBM) using the Advanced Normalization Tools (ANTs) to T1-weighted 3D MPRAGE brain MRI (voxel=1x1x3 mm3 ) from two clinical trials of OCR for RMS (Opera I: NCT01247324 and Opera II: NCT01412333). For each MRI visit, a Jacobian map was derived based on the deformation generated by registering the scan to a Single-Subject Template (SST) constructed for the same patient. Jacobian maps in patient SSTs were mapped to a population brain template for group analysis. Patients were included if MRI data were available for all visits (baseline, Weeks 24, 48, and 96) and the images were of sufficient quality to be successfully processed through the L-DBM pipeline (683 in the OCR arm, 580 in the active control arm of Interferon beta-1a). Voxelwise ANOVA was iterated over the whole brain to detect an interaction between Treatment and Time (p<0.01 with familywise error correction). Post-hoc analysis was done with mean Jacobian of identified regions, including Linear Mixed Effect (LME) analysis.
Results
Voxelwise ANOVA identified one cluster of voxels in the brain (besides the ventricles) that were contained within parts of thalamus, brainstem, and cerebellum. From the analysis of mean Jacobian of this cluster region, the percent volume reduction from baseline was significantly smaller in the treatment arm than the control arm at every follow-up visit (p<0.0001, t>8.0, relative difference>75%). LME analysis of the mean Jacobian of the cluster region confirmed that there was a strong interaction between Treatment and Time (F=18.35), for which age and sex were controlled.
Conclusions
L-DBM has identified several brain structures where atrophy was modified by the OCR treatment in RMS. The L-DBM analysis was performed at the voxel level and these regional findings are consistent with previous reports. Localization of the treatment-responsive brain structures may have an implication for future assessment of clinical outcomes in MS.