Primary Lung Adenocarcinoma With Trophoblastic Differentiation and Usage of Gefitinib for Postoperative Recurrence: Report of a Case With a Review of the Literature
Bibliographic record
Abstract
We report a rare and very aggressive case of lung carcinoma showing trophoblastic differentiation, so-called choriocarcinoma, for which postoperative recurrence was treated using gefitinib. A 51-year-old woman with pathological stage IIA adenocarcinoma with trophoblastic differentiation underwent left lower lobectomy with lymph node dissection and postoperative adjuvant chemotherapy, remaining tumor-free for 10 months until the detection of the brain metastasis. Gefitinib was administered, as the tumor was positive for epidermal growth factor receptor mutation, and this resulted in a 12-month progression-free period. The patient died 37 months after surgery due to multiple metastases in the brain, ovary and uterus. This is the first case report using gefitinib to the lung carcinoma showing trophoblastic differentiation. A review of the literature on lung cancer with trophoblastic differentiation suggested that a chemotherapeutic regimen for primary lung cancer other than germ cell tumor might be suitable for the lung carcinoma with trophoblastic differentiation. J Med Cases. 2016;7(11):484-487 doi: http://dx.doi.org/10.14740/jmc2662w
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".