Imaging Findings and MRI Patterns in a Cohort of 18q Chromosomal Abnormalities
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: The abnormalities of the long arm of chromosome 18 (18q) constitute a complex spectrum. We aimed to systematically analyze their MR imaging features. We hypothesized that there would be variable but recognizable white matter and structural patterns in this cohort. MATERIALS AND METHODS: In this retrospective cohort study, we included pediatric patients with a proved abnormality of 18q between 2000–2022. An age- and sex-matched control cohort was also constructed. RESULTS: Thirty-six cases, median MR imaging age 19.6 months (4.3–59.3), satisfied our inclusion criteria. Most were female (25, 69%, F:M ratio 2.2:1). Fifty MR imaging studies were analyzed, and 35 (70%) had delayed myelination. Two independent readers scored brain myelination with excellent interrater reliability. Three recognizable evolving MR imaging patterns with distinct age distributions and improving myelination scores were identified: Pelizaeus-Merzbacher disease–like (9.9 months, 37), intermediate (22 months, 48), and washed-out pattern (113.6 months, 53). Etiologically, MRIs were analyzed across 3 subgroups: 18q deletion (34, 69%), trisomy 18 (10, 21%), and ring chromosome 18 (5, 10%). Ring chromosome 18 had the highest myelination lag (27, P = .005) and multifocal white matter changes (P = .001). Trisomy 18 had smaller pons and cerebellar dimensions (anteposterior diameter pons, P = .002; corpus callosum vermis, P < .001; and transverse cerebellar diameter, P = .04). CONCLUSIONS: In this cohort of 18q chromosomal abnormalities, MR imaging revealed recognizable patterns correlating with improving brain myelination. Imaging findings appear to be on a continuum with more severe white matter abnormalities in ring chromosome 18 and greater prevalence of structural abnormalities of the pons and cerebellum in trisomy 18.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".