Review of renal cell carcinoma and its common subtypes in radiology
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Representing 2%-3% of adult cancers, renal cell carcinoma (RCC) accounts for 90% of renal malignancies and is the most lethal neoplasm of the urologic system. Over the last 65 years, the incidence of RCC has increased at a rate of 2% per year. The increased incidence is at least partly due to improved tumor detection secondary to greater availability of high-resolution cross-sectional imaging modalities over the last few decades. Most RCCs are asymptomatic at discovery and are detected as unexpected findings on imaging performed for unrelated clinical indications. The 2004 World Health Organization Classification of adult renal tumors stratifies RCC into several distinct histologic subtypes of which clear cell, papillary and chromophobe tumors account for 70%, 10%-15%, and 5%, respectively. Knowledge of the RCC subtype is important because the various subtypes are associated with different biologic behavior, prognosis and treatment options. Furthermore, the common RCC subtypes can often be discriminated non-invasively based on gross morphologic imaging appearances, signal intensity on T2-weighted magnetic resonance images, and the degree of tumor enhancement on dynamic contrast-enhanced computed tomography or magnetic resonance imaging examinations. In this article, we review the incidence and survival data, risk factors, clinical and biochemical findings, imaging findings, staging, differential diagnosis, management options and post-treatment follow-up of RCC, with attention focused on the common subtypes.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.006 | 0.001 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| 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 it