Spontaneous Dramatic Regression of Clear Cell Renal Cell Carcinoma After Pazopanib-Induced Severe Systemic Inflammatory Syndrome: A Case Report and Literature Review
Bibliographic record
Abstract
Renal cell carcinoma (RCC) is the most common type of kidney cancer, accounting for a significant proportion of all cancer cases in Korea. This case report presents a unique instance of spontaneous dramatic tumor regression in a 42-year-old Korean male diagnosed with clear cell RCC. The patient initially presented with right lower back pain, weight loss, and a loss of appetite. Following systemic immunotherapy with nivolumab and ipilimumab, and right radical nephrectomy, the patient was diagnosed with metastatic clear cell RCC, with new metastatic lesions detected in the liver, and on the chest wall on follow-up imaging. Second-line systemic treatment with pazopanib was initiated. Shortly thereafter, the patient developed severe systemic inflammatory syndrome, resulting in a mental stupor and acute kidney failure. Intensive care, including continuous renal replacement therapy and high-dose immunosuppressants, was administered. The patient's condition improved significantly with the intensive care regimen, leading to unintended tumor regression. These potentially fatal side effects occurred without infection, as confirmed by negative blood and urine cultures, and were attributed to the recent introduction of pazopanib. Follow-up imaging showed a significant reduction in hepatic metastatic lesions and the disappearance of chest wall nodules. This is the first reported case of RCC tumor regression following the side effects of pazopanib, underscoring the need for further studies into the immune mechanisms involved in RCC treatment and highlighting potential therapeutic strategies that leverage innate immune responses.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".