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Record W2966277873 · doi:10.1101/727818

Automated Separation of Diffusely Abnormal White Matter from Focal White Matter Lesions on MRI in Multiple Sclerosis

2019· preprint· en· W2966277873 on OpenAlexafffund
Josefina Maranzano, Mahsa Dadar, Douglas L. Arnold, D. Louis Collins, Sridar Narayanan

Bibliographic record

VenuebioRxiv (Cold Spring Harbor Laboratory) · 2019
Typepreprint
Languageen
FieldMedicine
TopicMultiple Sclerosis Research Studies
Canadian institutionsMcGill UniversityMontreal Neurological Institute and HospitalUniversité du Québec à Trois-Rivières
FundersCanadian Institutes of Health ResearchNovartis Pharmaceuticals Canada
KeywordsWhite matterFluid-attenuated inversion recoveryMagnetization transferMultiple sclerosisHyperintensityMagnetic resonance imagingHistopathologyMyelinNuclear medicineMedicinePathologyNuclear magnetic resonanceRadiologyPhysicsCentral nervous system

Abstract

fetched live from OpenAlex

ABSTRACT Background Previous histopathology and MRI studies have addressed the differences between focal white matter lesions (FWML) and diffusely abnormal white matter (DAWM) in multiple sclerosis (MS). These two categories of white matter T2-weighted (T2w) hyperintensity show different degrees of demyelination, axonal loss and immune cell density on histopathology, potentially offering distinct correlations with symptoms. Objectives 1) To automate the separation of FWML and DAWM using T2w MRI intensity thresholds and to investigate their differences in magnetization transfer ratios (MTR), which are sensitive to myelin content; 2) to correlate MTR values in FWML and DAWM with normalized signal intensity values on fluid attenuated inversion recovery (FLAIR), T2w, and T1-weighted (T1w) contrasts, as well as with the ratio of T2w/T1w normalized values, in order to determine whether these normalized intensities can be used as myelin-sensitive markers when MTR is not available. Methods Using similar 3T MRI protocols, 2 MS cohorts of 20 participants each were scanned in 2 centers, including: 3D T1w (MP2RAGE), 3D FLAIR, 2D T2w, and 3D magnetization-transfer (MT) contrasts. We used the first dataset to develop an automated technique to separate FWML from DAWM and the second one to validate the automation of the technique. We applied the automatic thresholds to both datasets and assessed the overlap of the manual and the automated masks using Dice kappa. We also assessed differences in mean MTR values between NAWM, DAWM and FWML, using manually and automatically derived masks in both datasets. Finally, we used the mean intensity of manually-traced areas of NAWM on T2w images as the normalization factor for each MRI contrast, and compared these with the normalized-intensity values obtained using automated NAWM (A-NAWM) masks as the normalization factor. Paired t-tests assessed the MTR differences across tissue types. Wilcoxon Signed Rank test and paired t-tests assessed FWML and DAWM differences in manual and automated derived volumes. Pearson correlations assessed the relationship between MTR and normalized intensity values in the manual and automated derived masks. Results The mean Dice-kappa values for dataset 1 were: 0.8 for DAWM masks and 0.7 for FWML masks. In dataset 2, mean dice-kappa values were: 0.8 for DAWM and 0.8 for FWML. Also, manual and automated DAWM and FWML volumes were not significantly different in both datasets. MTR values (expressed as mean and standard deviation, arbitrary units) were significantly lower in manually derived FWML compared with DAWM in both datasets: 1) FWML: 37.1 ± 3.2 vs DAWM: 43.3 ± 2.1; t=13.2; p<0.0001, and 2) FWML: 32.5 ± 3.9 vs DAWM: 38.8 ± 1.7; t=9.8; p<0.0001. Similar results were obtained using the automatic derived masks in both datasets: 1) FWML: 35.8 ± 2.8 vs DAWM: 43.1 ± 1.6; t=15.3; p<0.0001, and 2) FWML: 31.3 ± 3.1 vs DAWM: 38.1 ± 1.5; t=12.8; p<0.0001. MTR was also significantly lower in manually derived DAWM masks compared with normal appearing white matter (NAWM) in both datasets: 1) NAWM: 46.7 ± 1.3; t=10.1; p<0.0001, and 2) NAWM: 39.3 ± 0.8; t=3.1; p=0.003. Similar results were obtained using the automatic derived masks in both datasets: 1) NAWM: 46.3 ± 1.1; t=13.7; p<0.0001, and 2) NAWM: 39.9 ± 1.1; t=9.6; p<0.0001. In both datasets, manually derived FWML and DAWM MTR values showed significant correlations with normalized FLAIR (r=−0.35 to −0.67; p=0.06 to <0.0001), T2w (r=−0.60 to −0.72; p=0.003 to <0.0001), T1w/T2w (r=0.63 to 0.77; p=0.002 to <0.0001), and T1w (r=0.77 to 0.92; p<0.0001) intensities. Finally, normalized intensity values obtained using automatic derived masks were significantly correlated with the manually derived values in both datasets. Conclusions The separation of FWML and DAWM on MRI scans of MS patients using automated intensity thresholds on T2w images is feasible. MTR values are significantly lower in FWML than DAWM, and DAWM values are significantly lower than NAWM, reflecting potentially greater demyelination within focal lesions. Normalized intensity values of different MRI contrasts exhibit a significant correlation with MTR values in both tissues of interest and could be used as a proxy to assess demyelination when MTR images are not available.

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How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.003
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.007
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.001
Science and technology studies0.0000.000
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.042
GPT teacher head0.274
Teacher spread0.233 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreMethods

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations3
Published2019
Admission routes2
Has abstractyes

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