Definitions, outcomes, and management of hyperprogression in patients with non-small-cell lung cancer treated with immune checkpoint inhibitors
Bibliographic record
Abstract
BACKGROUND: The advent of immune checkpoint inhibitors (ICI) has been a breakthrough in the care of patients with non-small-cell lung cancers (NSCLC). However, physicians are now facing a previously unidentified clinical situation called hyperprogression (HP), which presents as a fast and unexpected increase in tumor burden. HP's existence and specificity to ICIs remains controversial because a widely acknowledged definition is currently lacking. Meanwhile, management remains elusive. METHODS: Medical records from all consecutive NSCLC patients who were treated with ICI from 2015 to 2018 were retrospectively analyzed. The HP incidence rate was calculated according to five definitions (tumor growth rate [TGR]ratio, ΔTGR, tumor growth kinetic [TGK], RECIST, and time to treatment failure [TTF]), and the agreement between such definitions was determined. The HP impact on overall survival (OS) was then assessed. The association between HP (defined using the TGRratio definition) and clinical and biological variables was also assessed. Clinical HP management and its impact on outcomes were described. RESULTS: We identified 169 consecutive ICI-treated patients, with potential HP accounting for 11.3 %, 5.7 %, 17.0 %, 9.6 %, and 31.7 % patients, according to TGRratio, ΔTGR, TGK, RECIST, and TTF definitions. Agreement between the different HP definitions was highly heterogeneous (range 29 %-77 %) and globally poor. HP was associated with shorter OS, compared to standard RECIST progressive disease, but this difference only reached statistical significance when using the TTF definition. TGRratio-based HP was significantly associated with hepatic metastases. In TGRratio-based HP patients, neither resuming chemotherapy nor corticosteroids use was associated with statistically significant impact on overall survival. CONCLUSION: We found fairly heterogeneous HP rates using different definitions. TTF was the only definition leading to significantly worsened OS. Further studies are needed to provide consensus recommendations for the assessment, definition, and management of HP, whose existence is likely real.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".