Abstract P4-03-05: Wide-field optical coherence tomography (WF-OCT) for near real-time, point-of-care assessment of margin status in breast-conserving surgery specimens: Results of a feasibility study at a high-volume single-centre
Bibliographic record
Abstract
Abstract Wide-Field Optical Coherence Tomography (WF-OCT) is a non-destructive, non-contact light imaging modality capable of label-free visualization of the internal microscopic architecture of breast tissue specimens. Its unique combination of high-resolution imaging in near real-time with tissue penetration depths approaching 2-mm makes it a promising imaging modality for obtaining detailed surgical margin status in breast-conserving surgery (BCS) specimens. A prototype WF-OCT imaging platform developed by Perimeter Medical Imaging, Inc. (Toronto, Canada) has permitted fully-automated, dynamically-focused visualization of margin widths around the intact surfaces of freshly excised BCS specimens. Herein are reported the results of a feasibility study at a high-volume single-centre evaluating the routine use of WF-OCT for sampling of surgical margin status in BCS specimens at the point-of-care. Methods: Women with biopsy confirmed breast cancer and scheduled for primary BCS were recruited at Princess Margaret Cancer Centre (Toronto, Canada). Standard medical care was not altered. Freshly excised BCS specimens including all lumpectomy samples were imaged by WF-OCT immediately prior to standard histological processing. The system acquired dynamically-focused, hemispherical coverage over two contra-lateral surfaces of the intact BCS specimen within the time constraints of the cold ischemic time window. High-resolution (10 μm) images of the tissue surface down to a 1 to 2-mm depth were obtained. Blinded assessments were performed on image data sets by two clinical readers (surgeon and radiologist) trained on a validated and unrelated data set correlating OCT images with histology slides. The readers were first asked to independently assess margin status using only blinded pre- and intra-operative knowledge (without OCT). Upon completion, the readers were provided OCT images of all scanned surface and similarly asked to assess the margin status with the additional OCT information. These assessments were subsequently evaluated by a breast pathologist comparing the OCT images and corresponding histopathology sections. The added utility of WF-OCT imaging information for margin prediction was studied. Results: [Pending study completion in August 2015]. Through accurate correlation with the histopathologic gold standard, OCT demonstrated capability to differentiate tissue microstructures, including: distinctive patterns for adipose tissue, fibrous stroma, breast lobules and ducts, cysts and microcysts, as well as in-situ and invasive carcinomas. Implications: The fully-automated WF-OCT imaging platform can integrate conveniently into standard pathological processing workflows to provide comprehensive sampling of surgical margin status in BCS specimens at the point-of-care. Clinical readers from surgical and radiological backgrounds can be trained to competently interpret WF-OCT images of BCS specimens for accurate prediction margin status. The implementation of WF-OCT at the point-of-care for routine surgical margin assessments will be further explored in future clinical trials. Citation Format: Valic MS, Leong WL, Done SJ, Wilson BC, Kulkarni S, McCready DR, Niu CJ, Atachia Y, Munro EA, Rempel D. Wide-field optical coherence tomography (WF-OCT) for near real-time, point-of-care assessment of margin status in breast-conserving surgery specimens: Results of a feasibility study at a high-volume single-centre. [abstract]. In: Proceedings of the Thirty-Eighth Annual CTRC-AACR San Antonio Breast Cancer Symposium: 2015 Dec 8-12; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2016;76(4 Suppl):Abstract nr P4-03-05.
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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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".