Use of imaging-based dosimetry for personalising radiopharmaceutical therapy of cancer
Bibliographic record
Abstract
Abstract Theranostics – i.e., the combination of molecular imaging and radiopharmaceutical therapy of cancer targeting a common biological feature – is a rapidly expanding field owing the recent successes of novel radiopharmaceutical therapies, such as 177Lu-based prostate-specific membrane antigen radioligand therapy of prostate cancer and peptide receptor radionuclide therapy of neuroendocrine tumours. Despite the ongoing technical developments in imaging-based dosimetry, the existence of tumour absorbed dose-efficacy and organ absorbed dose-toxicity relationships, as well as the high interpatient variability in absorbed doses per unit activity, radiopharmaceutical therapies are still mostly administered in a fixed-activity, one-size-fits-all fashion. This is at odds with the principles of radiation oncology, where the absorbed doses to tissues are prescribed and their delivery is carefully planned and controlled for each individual patient to maximise the clinical benefits. There is a growing body of clinical evidence that dosimetry-based radiopharmaceutical therapy allows to safely optimise tumour irradiation, which translates into improved clinical outcomes. In this narrative review, we will present the reported prospective clinical experience to date on the use of imaging-based dosimetry to personalise radiopharmaceutical therapies.
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 | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".