Dosimetric model for intraperitoneal targeted liposomal radioimmunotherapy of ovarian cancer micrometastases
Bibliographic record
Abstract
A simple model has been developed to investigate the dosimetry of micrometastases in the peritoneal cavity during intraperitoneal targeted liposomal radioimmunotherapy. The model is applied to free-floating tumours with radii between 0.005 cm and 0.1 cm. Tumour dose is assumed to come from two sources: free liposomes in solution in the peritoneal cavity and liposomes bound to the surface of the micrometastases. It is assumed that liposomes do not penetrate beyond the surface of the tumours and that the total amount of surface antigen does not change over the course of treatment. Integrated tumour doses are expressed as a function of biological parameters that describe the rates at which liposomes bind to and unbind from the tumour surface, the rate at which liposomes escape from the peritoneal cavity and the tumour surface antigen density. Integrated doses are translated into time-dependent tumour control probabilities (TCPs). The results of the work are illustrated in the context of a therapy in which liposomes labelled with Re-188 are targeted at ovarian cancer cells that express the surface antigen CA-125. The time required to produce a TCP of 95% is used to investigate the importance of the various parameters. The relative contributions of surface-bound radioactivity and unbound radioactivity are used to assess the conditions required for a targeted approach to provide an improvement over a non-targeted approach during intraperitoneal radiation therapy. Using Re-188 as the radionuclide, the model suggests that, for microscopic tumours, the relative importance of the surface-bound radioactivity increases with tumour size. This is evidenced by the requirement for larger antigen densities on smaller tumours to affect an improvement in the time required to produce a TCP of 95%. This is because for the smallest tumours considered, the unbound radioactivity is often capable of exerting a tumouricidal effect before the targeting agent has time to accumulate significantly on the tumour surface.
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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".