Abstract PO2-23-07: Three-dimensional navigated mass spectrometry for intraoperative margin assessment during breast cancer surgery
Bibliographic record
Abstract
Abstract Positive resection margins occur in approximately 25% of breast cancer (BCa) surgeries, requiring re-operation. Margin status is not routinely available during surgery; thus, technologies that identify residual cancer on the specimen or cavity are needed to provide intraoperative decision support that may reduce positive margin rates. Rapid evaporative ionization mass spectrometry (REIMS) is an emerging technique that chemically profiles the plume generated by tissue cauterization to classify the ablated tissue as either cancerous or non-cancerous, on the basis of detected lipid species. Although REIMS can distinguish cancer and non-cancerous breast tissue by the signals generated, it does not indicate the location of the classified tissue in real-time. Our objective was to combine REIMS with spatio-temporal navigation (navigated REIMS), and to compare performance of navigated REIMS with conventional histopathology examination (gold standard) to assess margin status (positive or negative) in patients undergoing surgery for BCa. A multivariate model was trained using pathology-validated mass spectra from ex vivo resection specimens from 11 patients, including 36 spectra from invasive cancer, and 118 spectra from normal breast adipose. This model was subsequently tested retrospectively on intraoperative data from 25 BCa cases that were conducted using navigated REIMS. Navigation was facilitated by an electromagnetic sensor placed on the cautery. To record the position of the tumor, a localization wire fitted with an electromagnetic sensor was placed into the tumor using ultrasound prior to surgery, and a 3D map of the tumor was created. This enabled real-time knowledge of the cautery position relative to the tumor during surgery. Spectra from the plume classified as BCa were mapped onto a display of the tumor region and compared with the pathology report. Our multivariate classifier exhibited >90% accuracy on cross-validation, driven by an elevated ratio of glycerophospholipid:triglyceride in cancer as compared with normal adipose. In the intraoperative testing cohort, 4/150 margins were assessed by pathology as positive, all of which were correctly identified by REIMS (Sensitivity=100%). Notably, two of these cases were positive for ductal carcinoma in situ. 146/150 margins were negative, of which 131/146 negative margins were consistent with histopathology (Specificity=90%). There were 13 negative margins that were determined to be positive by REIMS (false positive), of which 8 margins were noted as either ‘close margins’ (with cancer cells being detected within 1 mm of the inked margin on histopathology), or as high-density normal breast tissue. Other normal tissue such as skin and muscle also exhibited high phospholipid content but these spectra could be rationalized by relative distance from the tumor region using the navigation data and/or information provided by the surgeon in real-time. We have demonstrated the importance of spatio-temporal tracking and histopathology to validate intraoperative REIMS. Furthermore, we have shown the feasibility of navigated REIMS in identifying margins containing cancer in real-time during BCa surgery, where a wider excision may be desirable. Citation Format: Martin Kaufmann, Amoon Jamzad, Tamas Ungi, Jessica R Rodgers, Teaghan Koster, Chris Yeung, Josh Ehrlich, Alice Santilli, Mark Asselin, Natasja Janssen, Julie McMullen, Kathryn Solberg, Joanna Cheesman, Alessia Di Carlo, Kevin Yi Mi Ren, Sonal Varma, Shaila Merchant, Cecil Jay Engel, G Ross Walker, Andrea Gallo, Doris Jabs, Parvin Mousavi, Gabor Fichtinger, John F Rudan. Three-dimensional navigated mass spectrometry for intraoperative margin assessment during breast cancer surgery [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO2-23-07.
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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 | 0.001 |
| 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".