Lung Cancer Associated With Cystic Airspaces
Bibliographic record
Abstract
OBJECTIVE: The objectives of this study were to determine the frequency of lung cancers associated with a discrete cystic airspace and to characterize the morphologic and pathologic features of the cancer and the cystic airspace. MATERIALS AND METHODS: We reviewed all diagnosed cases of lung cancer resulting from baseline screening (n=595) and annual screening (n=111) in the International Early Lung Cancer Action Program to identify those abutting or in the wall of a cystic airspace. We also reviewed the pathologic specimens. RESULTS: A total of 26 lung cancers were identified abutting or in the wall of a cystic airspace. Of these, 13 were identified at baseline (13/595, 2%) and 13 at annual screening (13/111, 12%), which was significant (p<0.0001). The median circumferential portion of wall involved was less for the annual cancers than for the baseline ones, but this difference did not reach significance (90° vs 240°, p=0.07). The diagnosis was adenocarcinoma in all but three cases. Histologic analysis showed that the cystic space was a bulla, a fibrous walled cyst without a defined lining, or a pleural bleb and that in all but one case, the tumor was eccentric relative to the airspace and the wall of the airspace was unevenly thickened. CONCLUSION: At annual repeat CT screening, the finding of an isolated cystic airspace with increased wall thickness should raise the suspicion of lung cancer.
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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 | 0.002 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".