Manual Segmentation of MS Cortical Lesions Using MRI: A Comparison of 3 MRI Reading Protocols
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Double inversion recovery has been suggested as the MR imaging contrast of choice for segmenting cortical lesions in patients with multiple sclerosis. In this study, we sought to determine the utility of double inversion recovery for cortical lesion identification by comparing 3 MR imaging reading protocols that combine different MR imaging contrasts. MATERIALS AND METHODS: Twenty-five patients with relapsing-remitting MS and 3 with secondary-progressive MS were imaged with 3T MR imaging by using double inversion recovery, dual fast spin-echo proton-density/T2-weighted, 3D FLAIR, and 3D T1-weighted imaging sequences. Lesions affecting the cortex were manually segmented by using the following 3 MR imaging reading protocols: Protocol 1 (P1) used all available MR imaging contrasts; protocol 2 (P2) used all the available contrasts except for double inversion recovery; and protocol 3(P3) used only double inversion recovery. RESULTS: Six hundred forty-three cortical lesions were identified with P1 (mean = 22.96); 633, with P2 (mean = 22.6); and 280, with P3 (mean = 10). The counts obtained by using P1 and P2 were not significantly different (P = .93). The counts obtained by using P3 were significantly smaller than those obtained by using either P1 (P < .001) or P2 (P < .001). The intraclass correlation coefficients were P1 versus P2 = 0.989, P1 versus P3 = 0.615, and P2 versus P3 = 0.588. CONCLUSIONS: MR imaging cortical lesion segmentation can be performed by using 3D T1-weighted and 3D FLAIR images acquired with a 1-mm isotropic voxel size, supported by conventional T2-weighted and proton-density images with 3-mm-thick sections. Inclusion of double inversion recovery in this multimodal reading protocol did not significantly improve the cortical lesion identification rate. A multimodal approach is superior to using double inversion recovery alone.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".