Pulmonary Nodular Lymphoid Hyperplasia: Reviewing a Lung Cancer Mimicker
Bibliographic record
Abstract
Background: Pulmonary nodular lymphoid hyperplasia is a rare benign lymphoproliferative disorder. Patients are commonly asymptomatic. Its radiological presentation is characterized by peripheral single nodular lesion, multiple nodules or mass-like consolidation. Therefore, it can mimic other more frequent pulmonary diseases, such as lung cancer and pulmonary lymphoma. The diagnosis is often late, relying on histopathological findings. Case Presentation: We report a case of a 66-year-old woman who presented with cough and upper respiratory tract symptoms. Chest CT revealed a mass in the right lower lobe, mediastinal and hilar enlarged lymph nodes and multifocal ground-glass and part solid nodules. PET-CT findings were concerning for primary lung malignancy. She underwent transbronchial needle aspiration biopsy of the enlarged lymph nodes and pulmonary mass. The results were negative for malignancy, but immunophenotyping by flow cytometry was concerning for a lymphoproliferative disorder. For this reason, a percutaneous CT-guided transthoracic core needle biopsy of the mass was performed. Immunophenotypic features of lymphoma were not identified. Overall findings were suggestive of nodular lymphoid hyperplasia. The follow-up chest CT showed near complete resolution of the mass with a residual ground-glass lesion and stability of the enlarged lymph nodes, ground-glass and part solid nodules. Conclusion: Pulmonary nodular lymphoid hyperplasia is an uncommon lung disorder, usually detected incidentally on chest imaging. Its radiological features can mimic lung cancer and other more prevalent pulmonary diseases. Therefore, a multidisciplinary approach, including histopathological confirmation is essential for an accurate diagnosis. Although rare, it should be considered in the differential diagnosis of pulmonary malignancies.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| 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 | 0.002 |
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".