Chest CT Diagnosis and Clinical Management of Drug-related Pneumonitis in Patients Receiving Molecular Targeting Agents and Immune Checkpoint Inhibitors: A Position Paper from the Fleischner Society
Bibliographic record
Abstract
Use of molecular targeting agents and immune checkpoint inhibitors (ICIs) has increased the frequency and broadened the spectrum of lung toxicity, particularly in patients with cancer. The diagnosis of drug-related pneumonitis (DRP) is usually achieved by excluding other potential known causes. Awareness of the incidence and risk factors for DRP is becoming increasingly important. The severity of symptoms associated with DRP may range from mild or none to life-threatening with rapid progression to death. Imaging features of DRP should be assessed in consideration of the distribution of lung parenchymal abnormalities (radiologic pattern approach). The CT patterns reflect acute (diffuse alveolar damage) interstitial pneumonia and transient (simple pulmonary eosinophilia) lung abnormality, subacute interstitial disease (organizing pneumonia and hypersensitivity pneumonitis), and chronic interstitial disease (nonspecific interstitial pneumonia). A single drug can be associated with multiple radiologic patterns. Treatment of a patient suspected of having DRP generally consists of drug discontinuation, immunosuppressive therapy, or both, along with supportive measures eventually including supplemental oxygen and intensive care. In this position paper, the authors provide diagnostic criteria and management recommendations for DRP that should be of interest to radiologists, clinicians, clinical trialists, and trial sponsors, among others. This article is a simultaneous joint publication in Radiology and CHEST. The articles are identical except for stylistic changes in keeping with each journal’s style. Either version may be used in citing this article. Published under a CC BY 4.0 license. Online supplemental material is available for this article.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.002 | 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".