Cystic Primary Lung Cancer
Bibliographic record
Abstract
PURPOSE: Primary lung cancers associated with cystic airspaces are increasingly being recognized; however, there is a paucity of data on their natural history. We aimed to evaluate the prevalence, pathologic, and imaging characteristics of cystic lung cancer in a regional thoracic surgery center with a focus on the evolution of computed tomography morphology over time. MATERIALS AND METHODS: Consecutive patients referred for potential surgical management of primary lung cancer between January 2016 and December 2018 were included. Clinical, imaging, and pathologic data were collected at the time of diagnosis and at the time of the oldest computed tomography showing the target lesion. Descriptive analysis was carried out. RESULTS: A total of 441 cancers in 431 patients (185 males, 246 females), median age 69.6 years (interquartile range: 62.6 to 75.3 y), were assessed. Overall, 41/441 (9.3%) primary lung cancers were cystic at the time of diagnosis. The remaining showed solid (67%), part-solid (22%), and ground-glass (2%) morphologies. Histopathology of the cystic lung cancers at diagnosis included 31/41 (76%) adenocarcinomas, 8/41 (20%) squamous cell carcinomas, 1/41 (2%) adenosquamous carcinoma, and 1/41 (2%) unspecified non-small cell lung carcinoma. Overall, 8/34 (24%) cystic cancers at the time of diagnosis developed from different morphologic subtype precursor lesions, while 8/34 (24%) cystic precursor lesions also transitioned into part-solid or solid cancers at the time of diagnosis. CONCLUSIONS: This study demonstrates that cystic airspaces within lung cancers are not uncommon, and may be seen transiently as cancers evolve. Increased awareness of the spectrum of cystic lung cancer morphology is important to improve diagnostic accuracy and lung cancer management.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".