Improving Specificity of Breast MRI Using Prone PET and Fused MRI and PET 3D Volume Datasets
Bibliographic record
Abstract
UNLABELLED: MRI is a sensitive method for detecting invasive breast cancer, but it lacks specificity. To examine the effect of combining PET with MRI on breast lesion characterization, a prototype positioning device was fabricated to allow PET scans to be acquired in the same position as MRI scans--that is, prone. METHODS: To test the hypothesis that fusion of (18)F-FDG PET and MRI scans improves detection of breast cancer, 23 patients with suspected recurrent or new breast cancer underwent a routine whole-body PET scan, a prone PET scan of the chest, and a routine breast MRI scan. The attenuation-corrected prone PET and MRI datasets were registered twice by different operators. The fusion results were judged for quality by visual inspection and statistical analysis. A joint reading of the MRI and PET scans side by side and integrated images was performed by a nuclear medicine physician and a radiologist. Sensitivity and specificity of MRI and combined MRI and PET scans were calculated on the basis of pathology reports or at least 1 y of clinical and radiologic follow-up. RESULTS: All fusions were verified to be well matched using specific anatomic criteria. A total of 45 lesions was assessed. Lesion size range was 0.6 to 10.0 cm. Of the 44 breasts examined, 29 were suspicious for cancer, of which 15 were found to be positive on surgical excision. In lesion-by-lesion analysis, sensitivity and specificity of MRI alone were 92% and 52%, respectively; after MRI and PET fusion, they were 63% and 95%, respectively. The positive predictive value and the negative predictive value for MRI alone were 69% and 85%, respectively; after MRI and PET fusion, they were 94% and 69%, respectively. CONCLUSION: Acquisition of prone PET scans using the new positioning device permitted acquisition of prone scans suitable for fusion with breast MRI scans. Fused PET and MRI scans increased the specificity of MRI but decreased the sensitivity in this small group of patients. Additional data are needed to confirm the statistical significance of these preliminary findings.
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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 | 0.007 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| 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".