Is There a Role for Adjuvant Therapy After Surgery in “High Risk for Recurrence” Kidney Cancer? an Update on Current Concepts
Bibliographic record
Abstract
Background: Although surgical resection remains the standard of care for localized kidney cancers, a significant proportion of patients experience systemic recurrence after surgery and hence might benefit from effective adjuvant therapy. So far, several treatment options have been evaluated in adjuvant clinical trials, but only a few have provided promising results. Nevertheless, with the recent development of targeted therapy and immunomodulatory therapy, a series of clinical trials are in progress to evaluate the potential of those novel agents in the adjuvant setting. In this paper, we provide a narrative review of the progress in this field, and we summarize the results from recent adjuvant trials that have been completed. Methods: A literature search was conducted. The primary search strategy at the medline, Cochrane reviews, and http://ClinicalTrials.gov/databases included the keywords "adjuvant therapy," "renal cell carcinoma," and "targeted therapy or/and immunotherapy." Conclusions: Data from the s-trac study indicated that, in the "highest risk for recurrence" patient population, disease-free survival was increased with the use of adjuvant sunitinib compared with placebo. The assure trial showed no benefit for adjuvant sunitinib or sorafenib in the "intermediate- to high-risk" patient population. The ariser (adjuvant girentuximab) and protect (adjuvant pazopanib) trials indicated no survival benefit, but subgroup analyses in both trials recommended further investigation. The inconsistency in some of the current results can be attributed to a variety of factors pertaining to the lack of standardization across the trials. Nevertheless, patients in the "high risk of recurrence" category after surgery for their disease would benefit from a discussion about the potential benefits of adjuvant treatment and enrolment in ongoing adjuvant trials.
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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 | 0.009 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".