Novel patterns of response under immunotherapy
Bibliographic record
Abstract
Novel patterns of response and progression to immunotherapy have been reported that are not observed with conventional cytotoxic or targeted anticancer treatments. A major breakthrough with immunotherapy is its potential to achieve durable responses in a subset of patients with advanced cancer that can be maintained several years even after stopping the treatment. No standardized definition of durable response exists in the literature, and the optimal duration of treatment in case of durable response is not clearly established. However, the majority of patients do not respond to immunotherapy. Initially reported in advanced melanoma patients, pseudoprogression occurs when tumor index lesions regress after initial progression, supporting the concept of treating some patients beyond progression. Overall, reported rates of pseudoprogression never exceeded 10%, meaning that the large majority of patients who have a disease progression will not eventually respond to treatment. The decision to pursue treatment beyond progression must therefore only be taken in carefully selected patients with clinical benefit, who did not experience severe toxicities with immunotherapy. Conversely, rapid progressions, called hyperprogressions, were reported by several teams with rates ranging from 4% to 29%. These observations need to be confirmed from randomized trials. It is essential to interrupt the treatment in patients with hyperprogression, in order to switch to another potentially active treatment. Finally, some patients experience dissociated responses, with some lesions shrinking and others growing. Local treatment with surgery or radiotherapy for growing lesions may be considered. Several immune-specific-related response criteria were developed to better capture benefits of immunotherapy. These criteria only address the pseudoprogression pattern of response, and do not capture the other patterns of response such as hyperprogression and dissociated response. The classic RECIST remains a reasonable and meaningful method to assess response to immunotherapy in the clinic.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".