When tissue is not the only issue: Poorly differentiated lung squamous-cell carcinoma with adrenal, costochondral, and cardiac metastases – case report
Bibliographic record
Abstract
Nonmelanoma skin cancer is the most common cancer in the world, and lung cancer is the leading cause of death from cancer. Histologically, squamous cell carcinoma (SCC) is the second most prevalent type of both skin and lung cancers. We report the case of a 38-year-old female with metastatic, poorly differentiated lung SCC detected on chest X-ray after she presented to the hospital with cough and dyspnea. She had had a 7.5 cm moderately differentiated well-circumscribed posterior scalp SCC completely excised eight years earlier. CT scan showed a large right lung mass, nodular filling defect in the left atrium (LA), and metastases to the adrenal glands and the first rib. Her pulmonary tumor extends to the LA via the right superior pulmonary vein, which is rarely reported in the literature. Ultrasound-guided biopsy of the rib mass showed poorly differentiated SCC. The patient received urgent radiotherapy, given superior vena cava and mainstem bronchus compression. Head CT showed no brain metastasis. A biopsy of the left adrenal initially reported an undifferentiated pleomorphic sarcoma; however, a second pathologist reported it as a poorly differentiated carcinoma of lung origin. At least three pathologists verified the specimen, and it had a PD-L1 test with a 1-49% score. An initial echocardiogram confirmed the LA mass. The patient received a Paclitaxel-Carboplatin-Pembrolizumab regimen as the first-line treatment for metastatic SCC. A repeat echocardiogram after cycle 1 showed a decrease in the size of the tumor in the LA. Almost five months after her initial visit, this young woman’s symptoms and performance status have improved post-palliative radiotherapy and chemo-immunotherapy. Follow-up CT showed smaller lung, nodal, adrenal, and costochondral masses, and evidence of necrosis. This case is clinically relevant because it represents a common problem presenting uncommonly. Moreover, it highlights that ultrasound-guided interventions and medical imaging are essential in directing metastatic cancer diagnosis, treatment, and follow-up, especially when pathology cannot confirm but only presume a specific diagnosis.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.006 | 0.003 |
| 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".