Sarcoid-Like Granulomatosis of the Lung Related to Durvalumab After Chemoradiation for Pulmonary Squamous Cell Carcinoma
Bibliographic record
Abstract
Sarcoid-like granulomatosis is a unique immune-related adverse event (irAE) in cancer patients treated with immune checkpoint inhibitors (ICIs). This irAE is infrequent, reported to range from 2% to 22.2% of melanoma treated with ICI. In a case of granulomatosis localized in the lung, it is difficult to differentiate granulomatosis from cancer progression or metastases. Herein, we report a case of ICI-induced sarcoid-like granulomatosis of the lung, which was confusable with localized recurrence of the primary lung cancer. A 56-year-old woman with c-stage IIIA of pulmonary squamous cell carcinoma in the right lower lobe received chemo-radiotherapy with two courses of cisplatin and vinorelbine and concurrent thoracic irradiation, followed by 1-year durvalumab consolidation therapy. The tumor in the right S 6 grew and presented abnormal uptake by fluorodeoxyglucose positron emission tomography (FDG-PET), 1.5 years after durvalumab. Neither computed tomography (CT) nor FDG-PET found mediastinal and distant metastases. She underwent right lower lobe lobectomy. Histopathologically, the tumor and sampled lymph nodes contained no residue of carcinoma cells but presented diffuse epithelioid granuloma with infiltration of inflammatory cells, partial necrotic lesions and many multinucleated giant cells. In immunohistochemical stains, CD3 + and CD8 + T cells predominantly infiltrated, while there were few CD4 + T cells and a small number of CD20 + B cells. We followed her without steroid and other immunosuppressant drug. We should pay attention to the development of sarcoid-like granulomatosis as a rare irAE, which is difficult to be differentiated from cancer progression. J Med Cases. 2023;14(1):19-24 doi: https://doi.org/10.14740/jmc4038
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".