Feasibility of interstitial ex-vivo mammary autofluorescne microendoscopy
Bibliographic record
Abstract
In the past decades our knowledge of breast cancer has been rapidly evolving yet the basic paradigm of diagnosis and treatment of cancer has not. In cancer diagnosis, presentation of breast cancer can be a palpable lump or a suspicious mass on screening imaging, namely a mammogram. However, malignancy will be ascertained by tissue biopsy if needed. Biopsy is the gold standard breast cancer diagnostic test. Biopsy sampling is invasive, painful and costly. In addition, when the interpretation of current imaging modalities is not concordant with pathology results the biopsies may have to be repeated. Microendoscopy autofluorescence (AM) is a method of acquiring images directly from the tissues that contain fluorescent susceptible molecules (fluorophore). Studies of endoscopy in colon and esophagus showed that AM imaging is capable to recognize malignancy and can be utilized to discriminate between normal tissue and tumor. Additionally, it has been shown that, AM was able to differentiate cancer versus normal cells when a microendoscope was inserted into a breast duct. The main purpose of this study is to investigate if the same contrast exists if AM applied interstitially into the ex-vivo mastectomy breast tissues. This is a feasibility study to explore if interstitial AM has the potential to be coupled with breast cancer imaging diagnostics to provide better discrimination of the characteristics of the target tissue inside. The success in this approach could significantly reduce the number of required tissue biopsies to confirm the diagnosis.
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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 | 0.000 | 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 | 0.000 |
| 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 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".