Renal Cell Carcinoma With Cardiac Metastases
Bibliographic record
Abstract
The median survival of metastatic renal cell carcinoma (mRCC) is 5 months with a 1-year survival rate of 29%. Cardiac metastasis from RCC is a rare finding and there is scarce data available on treatment options. Recently, the combination of nivolumab and ipilimumab has been approved as a first-line treatment for advanced RCC in patients with a poor prognosis. Here we present a case of a 45-year-old male who presented to the emergency room with cough, dyspnea, and fever. Chest X-ray showed hilar lymphadenopathy and diffuse reticulonodular opacities, whereas a thoracic computed tomography (CT) scan revealed carcinomatosis lymphangitis, pleural carcinosis and multiple heterogenous zones on the cardiac wall. A transthoracic echocardiogram and a cardiac magnetic resonance imaging (MRI) revealed cardiac metastases. Subsequent imaging showed abundant distal metastases whereas a renal biopsy confirmed clear cell RCC making it a high-grade stage IV metastatic RCC. The patient was treated with the combination of nivolumab and ipilimumab. The unique feature about this case is that we have found a rare case of cardiac metastases that persists after a 3-month follow-up. Previously, there was only one case report of a patient with RCC and cardiac metastases who showed persistent response to nivolumab after 12 months. The key points from this case report are that a high index of suspicion is required for diagnosing cardiac metastases given that the signs and symptoms of metastatic cardiac involvement can be non-specific. Spread has been described as directly through the renal vein and vena cava or indirectly via the lymphatic system, which confers a worse prognosis. Furthermore, cardiac metastases can be mistaken for thrombi, endocarditis, or primary tumors, therefore echocardiograms can be limiting. Supplemental imaging with cardiac MRI or positron emission tomography/CT (PET/CT) is often needed for further characterization.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".