Feasibility Evaluation of Radioimmunoguided Surgery of Breast Cancer
Bibliographic record
Abstract
Breast-conserving surgery involves completely excising the tumour while limiting the amount of normal tissue removed, which is technically challenging to achieve, especially given the limited intraoperative guidance available to the surgeon. This study evaluates the feasibility of radioimmunoguided surgery (RIGS) to guide the detection and delineation of tumours intraoperatively. The 3D point-response function of a commercial gamma-ray-detecting probe (GDP) was determined as a function of radionuclide ((131)I, (111)In,( 99m)Tc), energy-window threshold, and collimator length (0.0-3.0-cm). This function was used to calculate the minimum detectable tumour volumes (MDTVs) and the minimum tumour-to-background activity concentration ratio (T:B) for effective delineation of a breast tumour model. The GDP had larger MDTVs and a higher minimum required T:B for tumour delineation with (131)I than with (111)In or (99m)Tc. It was shown that for (111)In there was a benefit to using a collimator length of 0.5-cm. For the model used, the minimum required T:B required for effective tumour delineation was 5.2 ± 0.4. RIGS has the potential to significantly improve the accuracy of breast-conserving surgery; however, before these benefits can be realized, novel radiopharmaceuticals need to be developed that have a higher specificity for cancerous tissue in vivo than what is currently available.
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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 | 0.002 |
| 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) | 0.001 | 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 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".