Skeletal Muscle Metastasis from Renal Cell Carcinoma: 21 cases and review of the literature
Bibliographic record
Abstract
OBJECTIVES: This study aimed to raise radiologists' awareness of skeletal muscle metastases (SMM) in renal cell carcinoma (RCC) cases and to clarify their imaging appearance. METHODS: A retrospective analysis was undertaken of 21 patients between 44-75 years old with 72 SMM treated from January 1990 to May 2009 at the MD Anderson Cancer Center in Houston, Texas, USA. Additionally, 37 patients with 44 SMM from a literature review were analysed. RESULTS: Among the 21 patients, the majority of SMM were asymptomatic and detected via computed tomography (CT). Mean metastasis size was 18.3 mm and the most common site was the trunk muscles (83.3%). The interval between discovery of the primary tumour and metastasis detection ranged up to 234 months. Peripheral enhancement (47.1%) was the most common post-contrast CT pattern and non-contrasted CT lesions were often isodense. Magnetic resonance imaging (MRI) characteristics were varied. Five lesions with available T1-weighted pre-contrast images were hyperintense to the surrounding muscle. Other organ metastases were present in 20 patients. Of the 44 SMM reported in the literature, the majority were symptomatic. Average metastasis size was 53.4 mm and only 20.5% of SMM were in trunk muscles. The average interval between tumour discovery and metastasis detection was 101 months. Other organ metastases were recorded in 17 out of 29 patients. CONCLUSION: SMM should always be considered in patients with RCC, even well after primary treatment. SMM from RCC may be invisible on CT without intravenous contrast; contrast-enhanced studies are therefore recommended. SMM are often hyperintense to the surrounding muscle on T1-weighted MRI scans.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 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".