Analysis of Spatial Heterogeneity of Responses in Metastatic Sites in Renal Cell Carcinoma Patients Treated with Nivolumab
Bibliographic record
Abstract
Background: The purpose was to determine whether tumor response to CPI varies by organ and to characterize response patterns in a group of surgically treated metastatic RCC patients treated with Nivolumab. Methods: A retrospective analysis was undertaken between January 2016 and March 2020 on patients receiving Nivolumab for metastatic RCC, following first-line therapy and having at least one baseline and two follow-up scans. A Fisher’s exact test was used to compare categorical variables, and a Kruskal–Wallis test was used to compare continuous variables. Results: Twenty-one out of thirty patients evaluated were eligible, and they were divided into two groups: responders (n = 11) and non-responders (n = 10). According to all iRECIST standards, 18 (85.7 percent) of the 21 patients had PD (10 patients), PR (3 patients), or SD (8 patients). At baseline, 7, 15, 4, 13, 7, and 7 patients, respectively, had detectable hepatic metastasis and lung, brain, lymph node, soft tissue, and other intra-abdominal metastases; these patients were evaluated for organ-specific response. The ORRs for hepatic metastasis and lung, brain, lymph node, soft tissue, adrenals, and other intraperitoneal metastases were correspondingly 10%, 20%, 35%, 0%, and 25%. In total, 13 (61.9%) of them demonstrated varied responses to CPI therapy, with 6 (28.5%) demonstrating intra-organ differential responses. The lymph nodes (35%) had the best objective response (BOR), followed by the adrenals and peritoneum (both 25%), the brain (20%), and the lung (20%). The response rate was highest in adrenal gland lesions (2/4; 50%), followed by lymph nodes (13/19; 68.4 percent) and liver (5/10; 50%), whereas rates were lowest for lesions in the lung (9/25; 36%), intraperitoneal metastases (1/4; 25%), and brain (1/5; 20%). Conclusions: In renal cell carcinoma, checkpoint inhibitors have a variable response at different metastatic sites, with the best response occurring in lymph nodes and the least occurring in soft tissue.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".