Fourth-Line Therapy in Metastatic Renal Cell Carcinoma (mRCC): Results from the International mRCC Database Consortium (IMDC)
Bibliographic record
Abstract
Background: Fourth-line therapy (4LT) in the treatment of metastatic renal cell carcinoma (mRCC) varies significantly due to the lack of data and recommendations to guide treatment decisions. Objective: To evaluate the use and efficacy of 4LT in mRCC patients. Methods: The International mRCC Database Consortium (IMDC) dataset was used to identify patients with mRCC treated with 4LT. This is a multicenter, retrospective cohort study. Overall survival (OS) and progression-free survival (PFS) were calculated using Kaplan-Meier curves. Patients were evaluated for overall response. The six prognostic variables included in the IMDC prognostic model were used to stratify patients into favorable-, intermediate- and poor-risk groups. Exploratory analyses were performed examining the elderly (>70 years old) and non-clear cell RCC subgroups. Proportional hazards regression modelling was performed adjusting these covariates by IMDC criteria measured at initiation of 4th line therapy. Results: 7498 patients were treated with first line targeted therapy and out of these 594 (7.9%) received 4LT. Everolimus was the most frequently used 4LT (16.8%). Sorafenib, axitinib, pazopanib, sunitinib and clinical trial drugs were also used in >10% of patients. The OS of patients on any 4LT was 12.8 months, with a PFS of 4.4 months. The overall response rate (ORR) was 13.7%. Favorable-risk patients using IMDC criteria (5%) displayed an OS of 23.1 months, intermediate-risk patients (66%) had an OS of 13.8 months and poor-risk patients (29%) had an OS of 7.8 ( p < 0.0001) months. Age >70 years and non-clear cell histology did not impact OS. Our study is limited by its retrospective design. Conclusions: 4LT use appears to have activity in mRCC patients. The IMDC continues to be of prognostic value in the fourth-line setting for OS. This study helps to set a benchmark for response rate and survival for which clinical trials can plan sample size calculations and aim to improve upon.
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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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".