Lung Density Analysis Using Quantitative Chest CT for Early Prediction of Chronic Lung Allograft Dysfunction
Bibliographic record
Abstract
BACKGROUND: Chronic lung allograft dysfunction (CLAD) limits long-term survival after lung transplantation (LTx). Early detection or prediction of CLAD can lead to changes in patient management that, in turn, may improve prognosis. The purpose of this study was to investigate the utility of quantitative computed tomography (CT) lung density analysis in early prediction of CLAD. METHODS: This retrospective cohort was drawn from all consecutive adult, first LTxs performed between 2006 and 2011. Post-transplant monitoring included scheduled surveillance bronchoscopies with concurrent pulmonary-functions tests and low-dose chest CT. Quantitative density metrics (QDM) derived from CT scans obtained at the time of 10%-19% decline in forced expiratory volume in 1 second (FEV1) were evaluated: 114 bilateral LTx recipients (66 with CLAD and 48 stable) and 23 single LTx recipients (11 with CLAD, 12 stable) were included in the analysis. RESULTS: In both single and double LTx, at the time of 10%-19% drop in FEV1 from baseline, the QDM was higher in patients who developed CLAD within 3 years compared with those patients who remained stable for at least 3.5 years. The area under the receiver operating characteristic curve (AUC) was 0.89 for predicting CLAD in single LTx and 0.63 in bilateral LTx. A multipredictor AUC accounting for FEV1, QDM, presence of consolidation, and ground glass opacities increased the AUC to 0.74 in double LTx. CONCLUSIONS: QDM derived from a CT histogram at the time of early drop in FEV1 may allow prediction of CLAD in patients after single or double LTx.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".