Magnetic resonance imaging biomarkers of chronic obstructive pulmonary disease prior to radiation therapy for non-small cell lung cancer
Bibliographic record
Abstract
OBJECTIVE: In this prospectively planned interim-analysis, the prevalence of chronic obstructive lung disease (COPD) phenotypes was determined using magnetic resonance imaging (MRI) and X-ray computed tomography (CT) in non-small-cell-lung-cancer (NSCLC) patients. MATERIALS AND METHODS: Stage-III-NSCLC patients provided written informed consent for pulmonary function tests, imaging and the 6-min-walk-test. Ventilation defect percent (VDP) and CT lung density (relative-of-CT-density-histogram <-950, RA950) were measured. Patients were classified into three subgroups based on qualitative and quantitative COPD and tumour-specific imaging phenotypes: (1) tumour-specific ventilation defects (TSD), (2) tumour-specific and other ventilation defects without emphysema (TSDV), and, (3) tumour-specific and other ventilation defects with emphysema (TSDVE). RESULTS: Seventeen stage-III NSCLC patients were evaluated (68 ± 7 years, 7 M/10 F, mean FEV1 = 77%pred) including seven current and 10 ex-smokers and eight patients with a prior lung disease diagnosis. There was a significant difference for smoking history (p = .02) and FEV1/FVC (p = .04) for subgroups classified using quantitative imaging. Patient subgroups classified using qualitative imaging findings were significantly different for emphysema (RA950, p < .001). There were significant relationships for whole-lung VDP (p < .05), but not RECIST or tumour-lobe VDP measurements with pulmonary function and exercise measurements. Preliminary analysis for non-tumour burden ventilation abnormalities using Reader-operator-characteristic (ROC) curves reflected a 94% classification rate for smoking pack-years, 93% for FEV1/FVC and 82% for RA950. ROC sensitivity/specificity/positive/negative likelihood ratios were also generated for pack-years, (0.92/0.80/4.6/0.3), FEV1/FVC (0.92/0.80/4.6/0.3), RA950 (0.92/0.80/4.6/0.3) and RECIST (0.58/0.80/2.9/1.1). CONCLUSIONS: In this prospectively planned interim-analysis of a larger clinical trial, NSCLC patients were classified based on COPD imaging phenotypes. A proof-of-concept evaluation showed that FEV1/FVC and smoking history identified NSCLC patients with ventilation abnormalities appropriate for functional lung avoidance radiotherapy.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".