Molecular imaging of cellular immunotherapies in experimental and therapeutic settings
Bibliographic record
Abstract
Cell-based cancer immunotherapies are becoming a routine part of the armamentarium against cancer. While remarkable successes have been seen, including durable remissions, not all patients will benefit from these therapies and many can suffer from life-threatening side effects. These differences in efficacy and safety across patients and across tumor types (e.g., blood vs. solid), are thought to be due to differences in how well the immune cells traffic to their target tissue (e.g., tumor, lymph nodes, etc.) whilst avoiding non-target tissues. Across patient variability can also stem from whether the cells interact with (i.e., communicate with) their intended target cells (e.g., cancer cells), as well as if they proliferate and survive long enough to yield potent and long-lasting therapeutic effects. However, many cell-based therapies are monitored by relatively simple blood tests that lack any spatial information and do not reflect how many immune cells have ended up at particular tissues. The ex vivo labeling and imaging of infused therapeutic immune cells can provide a more precise and dynamic understanding of whole-body immune cell biodistribution, expansion, viability, and activation status in individual patients. In recent years numerous cellular imaging technologies have been developed that may provide this much-needed information on immune cell fate. For this review, we summarize various ex vivo labeling and imaging approaches that allow for tracking of cellular immunotherapies for cancer. Our focus is on clinical imaging modalities and summarize the progression from experimental to therapeutic settings. The imaging information provided by these technologies can potentially be used for many purposes including improved real-time understanding of therapeutic efficacy and potential side effects in individual patients after cell infusion; the ability to more readily compare new therapeutic cell designs to current designs for various parameters such as improved trafficking to target tissues and avoidance of non-target tissues; and the long-term ability to identify patient populations that are likely to be positive responders and at low-risk of side effects.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".