The ‘Liaisons dangereuses’ Between Lung Cancer and Interstitial Lung Diseases: A Focus on Acute Exacerbation
Bibliographic record
Abstract
Patients with interstitial lung disease (ILD) are about five times more likely to develop lung cancer than those without ILD. The presence of ILD in lung cancer patients complicates diagnosis and management, resulting in lower survival rates. Diagnostic and treatment procedures needed for cancer can increase the risk of acute exacerbation (AE), one of the most severe complications for these patients. Bronchoscopic techniques are generally considered safe, but they can trigger AE-ILD, particularly after cryoprobe biopsies. Surgical procedures for lung cancer, including lung biopsies and resections, carry an elevated risk of AE-ILD. Postoperative complications and mortality rates highlight the importance of meticulous surgical planning and postoperative care. Furthermore, cancer treatments, such as chemotherapy, are all burdened by a risk of AE-ILD occurrence. Radiotherapy is important for managing both early-stage and advanced lung cancer, but it also poses risks. Stereotactic body radiation and particle beam therapies have varying degrees of safety, with the latter potentially offering a lower risk of AE. Percutaneous ablation techniques can help patients who are not eligible for surgery. However, these procedures may complicate ILD, and their associated risks still need to be fully understood, necessitating further research for improved safety. Overall, while advancements in lung cancer treatment have improved outcomes for many patients, the complexity of managing patients with concomitant ILD needs careful consideration and multidisciplinary assessment. This review provides a detailed evaluation of these risks, emphasizing the need for personalized treatment approaches and monitoring to improve patient outcomes in this challenging population.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".