Adjuvant and Neoadjuvant Small-Molecule Targeted Therapy in High-Risk Renal Cell Carcinoma
Bibliographic record
Abstract
Background: Non-localized renal cell carcinoma (rcc) carries a poor prognosis with a significant risk of mortality for patients. Traditionally, interleukin-2 and interferon alfa have been administered in this setting, with high toxicity and limited improvement in cancer-specific survival. However, newer agents such as sunitinib, sorafenib, bevacizumab, and temsirolimus have demonstrated great potential and provide a new frontier in the management of high-risk rcc. Methods: We queried PubMed and the Medline ovid databases for English articles from 1950 to December 2008 using the keywords “renal cell carcinoma,” “high risk” and “renal cell carcinoma,” and “neoadjuvant.” Articles from these searches and the reference lists of relevant articles were obtained. Articles published between 1996 and 2008 were included in the present review. Results: Risk stratification is imperative for optimal patient selection in adjuvant, neoadjuvant, and research settings. Utilization of interferon alfa and interleukin-2 has not demonstrated improved disease-free survival in the adjuvant setting. A number of adjuvant vaccines have also failed to demonstrate improved survival. The adjuvant role of targeted small-molecule inhibitors such as sorafenib, sunitinib, and temsirolimus is currently under investigation in phase iii trials. Sporadic case reports have demonstrated promising results with neoadjuvant use of these agents, and a pilot study of neoadjuvant temsirolimus is currently underway at our centre. Conclusions: The role, efficacy, and toxicity of adjuvant and neoadjuvant targeted small-molecule inhibitors in high-risk rcc remains to be delineated. Ideally, clinicians will be able to identify high-risk patients and offer treatment to those who would benefit most from adjuvant and neoadjuvant therapy, while minimizing toxicity in low-risk patients.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".