Efficacy of Pazopanib in the Treatment of Metastatic Malignant Giant Cell Tumor of Soft Tissue: A Case Report
Bibliographic record
Abstract
Giant cell tumor of soft tissue (GCT-ST), histologically resembling the GCT of the bone, is a rare tumor. The tumor has been categorized to have low malignancy. Few reports of local recurrence or distant metastasis and the use of chemotherapeutic agents for metastatic GCT-ST exist. Herein, we report the efficacy of pazopanib in a 78-year-old Japanese woman with GCT in the intrinsic back musculature with both post-operative local recurrence and lung metastasis. The patient visited the hospital with a three-month history of a palpable mass in the intrinsic back musculature. Following magnetic resonance imaging, the tumor predominantly exhibited slight hyperintensity on T2-weighted images and intense heterogeneous enhancement on contrast-enhanced T1-weighted images. A percutaneous needle biopsy was performed, and the pathological diagnosis was GCT-ST. The patient underwent surgery, and three months later she presented with not only local recurrence but also multiple lung metastases. The patient was immediately treated with pazopanib 400 mg once daily. One month after initiating treatment, a partial response in the pulmonary lesions was observed, and stable disease (SD) effects lasted for 11 months without severe adverse effects. Therefore, pazopanib treatment for metastatic malignant giant cell tumor of soft tissue achieved reasonable success.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 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".