Cytologic Features of Benign Solitary Pulmonary Nodules with Radiologic Correlation and Diagnostic Pitfalls
Bibliographic record
Abstract
BACKGROUND: The solitary pulmonary nodule (SPN) is a common radiologic abnormality often detected incidentally. The majority of SPNs represent benign processes, including granulotmatous inflammation, bronchogenic cysts and hamartomata. However, a solitary nodule may also potentially represent an early stage of lung cancer or a metastasis. Diagnostic procedures such as percutaneous fine needle aspiration biopsy can exclude malignancy in a majority of cases and may eliminate the need for more invasive surgical procedure. Correlation of the findings on the FNAB with radiologic features is helpful in establishing the benignity. CASES: We report the cytologic features of 6 cases of benign SPN: exogenous lipid pneumonia, sclerosing hemangioma, hemartoma, bronchogenic cyst, fungal granuloma and solitary fibrous tumor. We provide radiologic correlation for each entity and discuss the diagnostic pitfalls. CONCLUSION: Cytologically, lack of nuclear atypia with bland chromatin is useful in separating benign from malignant SPN. Radiologically, smaller lesions with smooth, well-defined margins and calcifications are more likely to be benign. Our cases illustrate the cytologic and immunohistochemical features that can help to make a more precise diagnosis. The identification of these features, when correlated with imaging findings, allows the cytopathologist to better approach the SPN.
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 | 0.001 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".