Renal Cell Carcinoma with Skin metastasis: A Case Report and Literature Review
Bibliographic record
Abstract
Background: Renal cell carcinoma (RCC) comprises 80% of primary renal cancers in the USA. Most cases are discovered incidentally during imaging for other reasons. The majority of patients are asymptomatic upon diagnosis, yet 25% have advanced disease. The most common locations for metastasis include liver, lymph nodes, bone, and lungs. RCC presents as skin metastasis in 6% of cases. Here, we present an even rarer occurrence: of recurrent RCC presenting with cutaneous metastases following nephrectomy and immunotherapy. Case Report: A 75 year-old white male presented to his physician for a yearly exam. Routine urinalysis revealed microcytic hematuria. Ultrasound demonstrated a solid mass in the upper pole of the right kidney. CT scan of the chest revealed pulmonary nodules. Patient underwent a right radical cyto-reductive nephrectomy, biopsy of mesenteric mass, and left thoracotomy with biopsy. Final pathology revealed grade 4 RCC, clear-cell type. Patient had treatment with interferon Alpha and then interleukin-2. As third line treatment, patient was switched to bevacizumab. While on treatment, patient presented a lesion on his left back and diffuse bone pain. During physical exam, a 2 x 2.5 cm firm, immobile, subcutaneous nodule was noted. Pathological analysis revealed a tumor consistent with metastatic RCC. MRI of the brain revealed calvarial lesions with some break into the scalp soft tissue and into the epidural space. Erlotinib was added to bevacizumab. Conclusion: This case helps highlight the ubiquity of RCC metastasis. The most common locations for metastasis include lung, liver, local lymph nodes, bone, and brain. In RCC, skin lesions have been described in the literature more specifically as pustules, painful and painless nodules, and macular lesions. The differential diagnosis of a skin lesion include non-specific drug reactions, opportunistic skin infections, and metastatic disease. The definite diagnosis of metastasis is made through tissue biopsy.
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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) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".