Does the Presence of Emphysema Increase the Risk of Radiation Pneumonitis in Lung Cancer Patients?
Bibliographic record
Abstract
Introduction Radiotherapy (rt) plays an important role in the treatment of lung cancer. One of the most common comorbidities in patients with lung cancer is pulmonary emphysema. The literature offers conflicting data about whether emphysema increases the occurrence and severity of radiation pneumonitis (rp). As a result, whether high doses of rt (with curative intent) should be avoided in patients with emphysema is still unclear. Objective We measured the documented incidence of rt in patients with and without emphysema who received curative radiation treatment. Methods This retrospective cohort study considered patients in the lung cancer clinical database of the Peter Brojde Lung Cancer Centre. Data from the database has been used previously for research studies, including a recent publication about emphysema grading, based on the percentage of lung occupied by emphysema on computed tomography (ct) imaging. Results Using previously published methods, chest ct imaging for 498 patients with lung cancer was scored for the presence of emphysema. The analysis considered 114 patients who received at least 30 Gy radiation. Of those 114 patients, 64 (56%) had emphysema, with approximately 23% having severe or very severe disease. The incidence of rp was 34.4% in patients with emphysema (n = 22) and 32.0% in patients with no emphysema (n = 16, p = 0.48). No difference in the incidence of rp was evident between patients with various grades of emphysema (p = 0.96). Similarly, no difference in the incidence of rp was evident between the two treatment protocols—that is, definitive rt 17 (37%) and combined chemotherapy–rt 21 (31%, p = 0.5). Conclusions In our cohort, the presence of emphysema on chest ct imaging was not associated with an increased risk of rp. That finding suggests that patients with lung cancer and emphysema should be offered rt when clinically indicated. However, further prospective studies will be needed for confirmation.
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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 | 0.001 |
| 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 | 0.000 | 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".