Association between dose to cardiac structures and overall survival: A multivariable analysis in a large, multi-institutional database of stage III NSCLC patients with external validation
Bibliographic record
Abstract
Background and Purpose Inconsistencies in identifying dose-limiting cardiovascular substructures for treating stage III non-small cell lung cancer (NSCLC) have hindered the implementation of cardiac sparing treatment planning guidelines. This study aims to address these inconsistencies by performing a multivariable survival analysis with overall survival as the endpoint using a large, multinational database, followed by external validation. Materials and Methods Clinical and dosimetric parameters from 1587 stage III NSCLC patients treated at five institutes were analyzed. The whole heart, four cardiac chambers, great vessels and their combinations were considered. The dataset was divided into a training set (four institutes) and a test set (one institute). The optimal parameter set was identified through cross-validation, and the resulting multivariable Cox regression model was externally validated using the test set. Adjusted hazard ratios (aHRs) for all cardiovascular parameters were evaluated. Results The strongest associations were found for low Dx% parameters. However, their incremental contribution to model performance, compared to clinical and lung dosimetric parameters only, was low, with small effect sizes. Specifically, the cardiovascular parameter identified by parameter selection was Left Side D5% (aHR: 1.007 Gy −1 , 95 % CI: 1.004 – 1.010 Gy −1 , p < 0.0001), which provided a slight improvement in model concordance index of 0.0062 (95 % CI: 0.0000–0.0127) in the training set and 0.0037 (95 % CI: −0.0200–0.0280) in the test set. Conclusions Although significant associations between cardiovascular parameters and survival were found, their small effect sizes should be considered when prioritizing cardiac sparing in stage III NSCLC treatment.
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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 | 0.008 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".