Imaging lobular breast carcinoma: comparison of synchrotron radiation DEI-CT technique with clinical CT, mammography and histology
Bibliographic record
Abstract
Different modalities for imaging cancer-bearing breast tissue samples are described and compared. The images include clinical mammograms and computed tomography (CT) images, CT images with partly coherent synchrotron radiation (SR), and CT and radiography images taken with SR using the diffraction enhanced imaging (DEI) method. The images are evaluated by a radiologist and compared with histopathological examination of the samples. Two cases of lobular carcinoma are studied in detail. The indications of cancer are very weak or invisible in the conventional images, but the morphological changes due to invasion of cancer become pronounced in the images taken by the DEI method. The strands penetrating adipose tissue are seen clearly in the DEI-CT images, and the histopathology confirms that some strands contain the so-called 'Indian file' formations of cancer cells. The radiation dose is carefully measured for each of the imaging modalities. The mean glandular dose (MGD) for 50% glandular breast tissue is about 1 mGy in conventional mammography and less than 0.25 mGy in projection DEI, while in the clinical CT imaging the MGD is very high, about 45 mGy. The entrance dose of 95 mGy in DEI-CT imaging gives rise to an MGD of 40 mGy, but the dose may be reduced by an order of magnitude, because the contrast is very large in most images.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".