Development and evaluation of pulmonary imaging multi-parametric response maps for deep phenotyping of chronic obstructive pulmonary disease
Bibliographic record
Abstract
Objective: Our aim was to develop and evaluate multi-parametric response maps derived from pulmonary x-ray computed tomography (CT), 1H and hyperpolarized 3He static ventilation and diffusion-weighted magnetic resonance imaging (MRI). These maps were generated to phenotype patients with chronic obstructive pulmonary disease (COPD) based on the presence of airways disease, air trapping, emphysema, alveolar distension, and ventilation defects. Methods: To generate thoracic imaging multi-parametric response maps (mPRM), multispectral 1H, 3He and CT images were segmented and co-registered. 1H and 3He MR images were segmented using a semi-automated segmentation algorithm, the diffusion weighted MR images were segmented using a threshold-based algorithm and CT images were segmented using Pulmonary Workstation 2.0 (VIDA Diagnostics, Coralville, IA). The volume-matched segmented 1H/3He maps were registered using landmark rigid registration. The 3He maps/the diffusion weighted images were registered using an intensity-based rigid registration. CT-to-MRI co-registration was achieved using modality-independent neighborhood descriptor (MIND) deformable registration; inspiratory and expiratory CT were co-registered using an affine registration with a deformable step provided by the NiftyReg toolkit. The co-registered thoracic maps were used to generate multiparametric maps. Results: mPRM maps were generated for six different voxel classifications with increasing disease abnormality/severity as follows: 1) ventilated voxels with >-856HU/>-950HU and normal apparent diffusion coefficient (ADC) values, 2) ventilated voxels with <-856HU/<-950HU and abnormal ADC values, 3) ventilated voxels with >-856HU/>-950HU and normal ADC values, 4) ventilated voxels with <-856HU/<-950HU and abnormal ADC values, 5) unventilated voxels with >-856HU/>-950HU, and, 6) unventilated voxels with <-856HU/<-950HU. Conclusion: mPRM measurements were automated in a dedicated pipeline for MRI and CT measurements to phenotype COPD patients.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".