Utility of MDCT MIP Postprocessing Reconstruction Images in Children With Hereditary Hemorrhagic Telangiectasia
Bibliographic record
Abstract
OBJECTIVE: The aim of the study was to evaluate whether maximum intensity projection (MIP) images improve the detection and the delineation of the anatomic makeup of pulmonary nodules and/or arteriovenous malformations (pAVMs) in children with hereditary hemorrhagic telangiectasia (HHT). MATERIALS AND METHODS: Two radiologists (D.M., E.I.C.) performed a blinded review of chest multidetector computed tomography scans in 39 children (age, 0-18 years) with proven HHT. Multiplanar 2.5 mm slices were blindly compared with multiplanar MIP for the presence of nodules and/or overt pAVMs and for the ability to identify vessels associated with the pAVMs. Parameters that were assessed included number of definitive nodules, number of definitive pAVMs, and the ability to detect the feeding artery or draining vein in both conventional and MIP images. RESULTS: Our study showed similar detection rates between axial scans and MIP images for the detection of nodules (axial R1: 75 vs 62, P = 0.05; MIPS: 78 vs 86, P = 0.05) and in the determination of definite pAVMS (axials: 21 vs 29, P = 0.0007; MIPS: 27 vs 35, P = 0.01). Statistically significant differences were obtained in the ability to identify the feeding artery and draining vein between standard 2.5 mm slices and MIP images (axials: 13 vs 13, P = 0.0008; MIPS: 27 vs 23, P = 0.01). No other data parameters achieved statistically significance. CONCLUSIONS: Maximum intensity projection images in children with HHT can help identify the presence and the anatomy of pAVMs for future embolization.
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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 | 0.001 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 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".