Time‐resolved fluorescence (TRF) and diffuse reflectance spectroscopy (DRS) for margin analysis in breast cancer
Bibliographic record
Abstract
PURPOSE: One of the major problems in breast cancer surgery is defining surgical margins and establishing complete tumor excision within a single surgical procedure. The goal of this work is to establish instrumentation that can differentiate between tumor and normal breast tissue with the potential to be implemented in vivo during a surgical procedure. METHODS: A time-resolved fluorescence and reflectance spectroscopy (tr-FRS) system is used to measure fluorescence intensity and lifetime as well as collect diffuse reflectance (DR) of breast tissue, which can subsequently be used to extract optical properties (absorption and reduced scatter coefficient) of the tissue. The tr-FRS data obtained from patients with Invasive Ductal Carcinoma (IDC) whom have undergone lumpectomy and mastectomy surgeries is presented. A preliminary study was conducted to determine the validity of using banked pre-frozen breast tissue samples to study the fluorescence response and optical properties. Once the validity was established, the tr-FRS system was used on a data-set of 40 pre-frozen matched pair cases to differentiate between tumor and normal breast tissue. All measurements have been conducted on excised normal and tumor breast samples post surgery. RESULTS: Our results showed the process of freezing and thawing did not cause any significant differences between fresh and pre-frozen normal or tumor breast tissue. The tr-FRS optical data obtained from 40 banked matched pairs showed significant differences between normal and tumor breast tissue. CONCLUSION: The work detailed in the main study showed the tr-FRS system has the potential to differentiate malignant from normal breast tissue in women undergoing surgery for known invasive ductal carcinoma. With further work, this successful outcome may result in the development of an accurate intraoperative real-time margin assessment system. Lasers Surg. Med. 50:236-245, 2018. © 2018 Wiley Periodicals, Inc.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".