Adaptation and Clinical Validation of a New Handheld Optical Imaging Device (PRODIGIâ¢) and Workflow for Real-time Intra-operative Margin Assessment in Breast Cancer
Bibliographic record
Abstract
Background: We report here early attempts of adapting a prototype fluorescence imaging system (PRODIGI™) to be used as a surgical guidance tool to improve margin-detection in breast cancer.\n\nMethods: 36 patients were recruited to study the autofluorescence characteristics of ex vivo specimens. 5-ALA (20 mg/kg) was used as a contrast agent in human breast cancer cell lines and xenograft tumour models to detect PpIX fluorescence.\n\nResults: Administrative approvals were obtained and a surgical drape was used for sterilization. PRODIGITM could differentiate between normal and tumour tissues based on autofluorescence alone in ex vivo samples. PpIX signal was detected in experimental mice, and absent in control mice. The threshold of detection was on the order of 10 nM.\n\nConclusions: Autofluorescence alone with PRODIGI™ was not sufficient for margin assessment of ex vivo breast tumour surgical specimens. 5-ALA at an optimal dosage may be adopted as a contrast agent to enhance tumour signal.
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 | 0.003 | 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".