Exploring the Frequency and Risk Factors of Hyperprogressive Disease in Patients with Advanced Melanoma Treated with Immune Checkpoint Inhibitors
Bibliographic record
Abstract
Hyperprogressive disease (HPD) is described as the unexpected rapid growth of a tumour accompanied by a decline in performance status. While immune checkpoint inhibitors (ICIs) have improved outcomes in advanced melanoma, HPD remains a significant challenge in a subset of patients. Although HPD has been extensively studied in various solid tumours, research specifically focusing on advanced melanoma remains limited. We analysed 158 advanced melanoma patients, with 66.5% (n = 105) receiving anti-PD-1 and 33.5% (n = 53) receiving nivolumab plus ipilimumab. The median overall survival was 4.9 months for patients with HPD compared to 8.9 months for those with progressive disease without HPD (p = 0.014). Factors associated with HPD included liver metastasis (p = 0.002), three or more metastatic sites (p < 0.001), elevated lactate dehydrogenase levels (p = 0.004), and Eastern cooperative oncology group performance status ≥2 (p = 0.023). Multivariate analysis identified the Royal Marsden Hospital score (HR 3.675, 95% CI: 1.166–11.580, p = 0.026) as an independent risk factor for HPD, with the MDA-ICI score also trending towards significance (HR 4.466, 95% CI: 0.947–21.061, p = 0.059). This study provides valuable insights into the frequency and factors associated with HPD in advanced melanoma patients treated with ICIs, highlighting the relevance of clinical markers and scoring systems in predicting HPD risk.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.001 |
| 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".