Use of imaging-based dosimetry for personalising radiopharmaceutical therapy of cancer
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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 177 Lu-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.
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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) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it