MSACL 2026 Abstract

Clinical Utility of the Intelligent Knife Sampling Device for Intraoperative Margin Assessment: A Prospective Case Series of 66 Breast Cancer Surgeries

Martin Kaufmann, Amoon Jamzad, Chris Yeung, Tamas Ungi, Amira Othman, Sonal Varma, Kevin Y-M Ren, Shaila Merchant, G Ross Walker, C Jay Engel, Andrea Gallo, Doris Jabs, Teaghan Koster, Jessica R Rodgers, Natasja Janssen, Julie McMullen, Kathryn Solberg, Joanna Cheesman, Parvin Mousavi, Gabor Fichtinger, John F Rudan
Queen's University

Martin Kaufmann, PhD (Presenter) Queen’s University

Presenter Bio: Martin is a research associate at Queen’s University in Kingston, Ontario, Canada. Working with a multidisciplinary team of clinicians and basic scientists, Martin’s research involves the application of LC-MS/MS to the study of vitamin D metabolism, and role of in-born errors of vitamin D metabolism in hypercalcemic disorders. Other research interests include the use of REIMS and DESI to support tumor profiling studies, towards the development of diagnostic tools in the operating room and pathology lab. Martin has played an important role in establishing metabolomics facilities at Queen’s, and has been involved in the training of over 30 students in project-based courses involving mass spectrometry.

Relevant Financial Disclosures (within past 24 months, reported on Apr 21, 2026) No relevant financial relationship(s) to disclose.

INTRODUCTION:
Positive margins occur in 25% of breast cancer (BCa) surgeries, requiring re-operation. Margin status is not routinely available during surgery; thus, new technologies that identify residual cancer on the specimen or cavity are needed to provide intraoperative decision support. Rapid evaporative ionization mass spectrometry (REIMS research system, aka. Intelligent Knife sampling device) is a promising technology that chemically profiles the plume generated by cauterization, which can be used to classify the ablated tissue as either cancerous or non-cancerous, on the basis of differential abundance of lipids. Although the REIMS research system has demonstrated high sensitivity and specificity in ex vivo studies, the clinical utility of the device has not yet been evaluated intraoperatively.

OBJECTIVE:
We set out to characterize in vivo breast tissue using the REIMS research system during breast cancer surgery, and test the sensitivity and specificity of tissue classification algorithms at determining margin status.

METHODS:
66 Patient participants were enrolled, and received standard-of-care surgery at Kingston Health Sciences Centre, where a mobile REIMS research system was placed in the operating room to continuously sample the electrosurgical plume during surgery. We used a combination of surgeon call-outs, and synchronized electromagnetic tracking of the cautery to assign tissue-type labels to intraoperative spectra, and to compute the orientation and distance of the intraoperative sampling points with respect to the ultrasound-mapped tumor. During a subset of 11 surgeries, ex vivo point burns were also conducted on the resection specimen to generate a pathology-validated database of spectra from cancerous (N=39) and non-cancerous tissue (N=118); which were used to train a PCA/LDA model that was then used to classify spectra from the intraoperative plume (>90% accuracy on cross-validation). Margin status determined using the REIMS research system was assigned post-hoc using the output of the classifier, and compared with the histopathology report which served as the gold standard.

RESULTS:
11/13 positive margins were correctly assigned, comprising both invasive and in situ disease (Sensitivity=85%). Spectral profiles from positive margins exhibited an increased ratio of phospholipid-to-triglycerides (PL:TG), as compared with normal breast adipose, which comprised mostly of TG. 287/334 TG-rich negative margins were also correctly identified (Specificity=86%). Review of the 47 false positives, revealed cauterization of fibrous tissue, benign ducts, skin, and muscle/chest wall, which are also rich in PL. In the two positive margin cases determined to be normal by our classifier (false negatives), increased PL was observed, but to a lesser degree than the true positive margins. Interestingly, 8 false positives occurred at close margins, where cancer cells were observed within 2 mm of the inked margin. 38% of all close margins were flagged by REIMS. Positive predictive value and negative predictive value were 19, and 99%.

CONCLUSIONS:
We have demonstrated the utility of the REIMS research system for identifying positive margins during breast cancer surgery, as well as certain clinically-actionable close margins. Providing real-time decision support during surgery using spatially-aware, navigated mass spectrometry is anticipated to reduce the frequency of positive margins, as well as reduce the amount of normal tissue that is removed.