Feature extraction and classification of dynamic contrast-enhanced T2*-weighted breast image data
Bibliographic record
Abstract
UNLABELLED: The relatively low specificity of dynamic contrast-enhanced T1-weighted magnetic resonance imaging (MR) imaging of breast cancer has lead several groups to investigate different approaches to data acquisition, one of them being the use of rapid T2*-weighted imaging. Analyses of such data are difficult due to susceptibility artifacts and breathing motion. MATERIALS AND METHODS: One-hundred-twenty-seven patients with breast tumors underwent MR examination with rapid, single-slice T2*-weighted imaging of the tumor. Different methods for classifying the image data set using leave-one-out cross validation were tested. Furthermore, a semi-automatic region of interest (ROI) definition tool was presented and compared with manual ROI definitions from a previous study. Finally, pixel-by-pixel analysis was done and compared with ROI analysis. The analyses were done with and without noise reduction. RESULTS: The minimum enhancement parameter was the most robust and accurate of the parameters tested. The semi-automatic ROI definition method was fast and produced similar results as the manually defined ROIs. Noise reduction improved both sensitivity and specificity, but the improvement was not statistically significant. The pixel-based analysis methods used in the present study did not improve classification results. CONCLUSIONS: Analysis of T2*-weighted breast images can be done in a rapid and robust manner by using semi-automatic ROI definition tools in combination with noise reduction. Minimum enhancement gives an indication of malignancy in T2*-weighted imaging.
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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 | 0.001 |
| 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 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".