Early clinical results from PEM 1: a high resolution system for positron emission mammography
Bibliographic record
Abstract
The authors report first results of the stage one clinical trial performed with the Positron Emission Mammography-1(PEM-1) scanner. Patients selected are >20 years of age with no history of treatment for cancer, recently diagnosed with an unclassified tumour mass of <2 cm diameter in one breast. A 2 mCi (74 MBq) injection of 2-[18F]-fluoro-deoxy-2-D-glucose (FDG) is given 40 minutes prior to scanning. The scan is a two step process. (1) The patient's mammogram is obtained with the tumour and a co-registration tool in the field-of-view (FOV). (2) Without moving the patient, the PEM detectors are moved in and emission data is collected for up to 5 minutes. The mammogram is digitized and fused with the reconstructed PEM image. To date, the authors have scanned eleven patients. In one subject a tumour <1 cm in diameter was localised visually as a hot spot 1.2 cm/spl times/0.8 cm in two dimensions. There was close to 4:1 increase in the focal uptake of FDG at the tumour site. Knowing the thickness of the compressed breast one can locate the tumour depth within the breast. There have been five true-positive results/spl times/these subjects were all correctly diagnosed by PEM-1 as having breast cancer; four true-negative results these subjects tested negative for breast cancer with PEM-1 and were surgically confirmed as being cancer free. There have been two false-negative results-the reasons for these are discussed in the text. The PEM-1 system is a high resolution, high efficiency and affordable system which can produce emission images accurately co-registered with X-ray mammographic images using at most 1/5 the F18-FDG and imaging for at most 1/5 the time required when conventional PET instruments are used.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.002 | 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".