Advancements in diagnosis and treatment of lung adenocarcinoma patients under ECMO support: a case series and comprehensive literature review
Bibliographic record
Abstract
This case series explores the use of veno-venous extracorporeal membrane oxygenation (VV ECMO) as a bridge to diagnosis and treatment in patients with suspected lung adenocarcinoma and severe respiratory failure refractory to conventional mechanical ventilation. We present three cases where ECMO support was initiated to facilitate further diagnostic evaluations, including lung biopsy and bronchoalveolar lavage fluid liquid biopsy, enabling the identification of actionable mutations and guiding targeted therapies. Two patients achieved significant clinical improvement and were successfully weaned from ECMO, while one patient experienced an unfavorable outcome due to a combination of uncontrolled tumor progression and hospital-acquired pneumonia. Our findings suggest that ECMO, traditionally considered a relative contraindication in advanced cancer, can be a valuable temporary support in carefully selected patients, allowing time for diagnosis and targeted treatment initiation. Furthermore, our experience highlights the feasibility and safety of performing lung biopsies under ECMO with appropriate anticoagulation management. This study emphasizes the importance of individualized decision-making and a multidisciplinary approach when considering ECMO for lung cancer patients, carefully weighing the risks of infection and disease progression to optimize patient outcomes.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".