Management of Lung Cancer in the Patient with Interstitial Lung Disease
Bibliographic record
Abstract
Patients with interstitial lung disease (ILD), especially those with pulmonary fibrosis, are at increased risk of developing lung cancer. Management of lung cancer in patients with ILD is particularly challenging. Diagnosis can be complicated by difficulty differentiating lung nodules from areas of focal fibrosis, and percutaneous biopsy approaches confer an increased risk of complications in those with pulmonary fibrosis. Lung cancer treatment in these patients pose several specific considerations. The degree of lung function impairment may preclude lobectomy or surgical resection of any type. Surgical resection can trigger an acute exacerbation of the underlying ILD. The presence of ILD confers an increased risk of pneumonitis with radiotherapy, and many of the systemic therapies also carry an increased risk of pneumonitis in this population. The safety of immunotherapy in the setting of ILD remains to be fully elucidated and concerns remain as to triggering pneumonitis. The purpose of this review is to summarize the evidence regarding consideration for tissue diagnosis, chemotherapy and immunotherapy, radiotherapy, and surgery, in this patient population and discuss emerging areas of research. We also propose a multidisciplinary approach and practical considerations for monitoring for ILD progression during lung cancer 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".