Rapid evaporative ionization mass spectrometry (REIMS) is an emerging technique that allows near-real-time characterization of human tissue in vivo by analysis of the aerosol ("smoke") released during electrosurgical dissection. The coupling of REIMS technology with electrosurgery for tissue diagnostics is known as the intelligent knife (iKnife). This study aimed to validate the technique by applying it to the analysis of fresh human tissue samples ex vivo and to demonstrate the translation to real-time use in vivo in a surgical environment. A variety of tissue samples from 302 patients were analyzed in the laboratory, resulting in 1624 cancerous and 1309 noncancerous database entries. The technology was then transferred to the operating theater, where the device was coupled to existing electrosurgical equipment to collect data during a total of 81 resections. Mass spectrometric data were analyzed using multivariate statistical methods, including principal components analysis (PCA) and linear discriminant analysis (LDA), and a spectral identification algorithm using a similar approach was implemented. The REIMS approach differentiated accurately between distinct histological and histopathological tissue types, with malignant tissues yielding chemical characteristics specific to their histopathological subtypes. Tissue identification via intraoperative REIMS matched the postoperative histological diagnosis in 100% (all 81) of the cases studied. The mass spectra reflected lipidomic profiles that varied between distinct histological tumor types and also between primary and metastatic tumors. Thus, in addition to real-time diagnostic information, the spectra provided additional information on divergent tumor biochemistry that may have mechanistic importance in cancer.

BackgroundRe-operation for positive resection margins following breast-conserving surgery occurs frequently (average = 20–25%), is cost-inefficient, and leads to physical and psychological morbidity. Current margin assessment techniques are slow and labour intensive. Rapid evaporative ionisation mass spectrometry (REIMS) rapidly identifies dissected tissues by determination of tissue structural lipid profiles through on-line chemical analysis of electrosurgical aerosol toward real-time margin assessment.MethodsElectrosurgical aerosol produced from ex-vivo and in-vivo breast samples was aspirated into a mass spectrometer (MS) using a monopolar hand-piece. Tissue identification results obtained by multivariate statistical analysis of MS data were validated by histopathology. Ex-vivo classification models were constructed from a mass spectral database of normal and tumour breast samples. Univariate and tandem MS analysis of significant peaks was conducted to identify biochemical differences between normal and cancerous tissues. An ex-vivo classification model was used in combination with bespoke recognition software, as an intelligent knife (iKnife), to predict the diagnosis for an ex-vivo validation set. Intraoperative REIMS data were acquired during breast surgery and time-synchronized to operative videos.ResultsA classification model using histologically validated spectral data acquired from 932 sampling points in normal tissue and 226 in tumour tissue provided 93.4% sensitivity and 94.9% specificity. Tandem MS identified 63 phospholipids and 6 triglyceride species responsible for 24 spectral differences between tissue types. iKnife recognition accuracy with 260 newly acquired fresh and frozen breast tissue specimens (normal n = 161, tumour n = 99) provided sensitivity of 90.9% and specificity of 98.8%. The ex-vivo and intra-operative method produced visually comparable high intensity spectra. iKnife interpretation of intra-operative electrosurgical vapours, including data acquisition and analysis was possible within a mean of 1.80 seconds (SD ±0.40).ConclusionsThe REIMS method has been optimised for real-time iKnife analysis of heterogeneous breast tissues based on subtle changes in lipid metabolism, and the results suggest spectral analysis is both accurate and rapid. Proof-of-concept data demonstrate the iKnife method is capable of online intraoperative data collection and analysis. Further validation studies are required to determine the accuracy of intra-operative REIMS for oncological margin assessment.Electronic supplementary materialThe online version of this article (doi:10.1186/s13058-017-0845-2) contains supplementary material, which is available to authorized users.

Breast cancer is a heterogeneous disease characterized by varying responses to therapeutic agents and significant differences in long-term survival. Thus, there remains an unmet need for early diagnostic and prognostic tools and improved histologic characterization for more accurate disease stratification and personalized therapeutic intervention. This study evaluated a comprehensive metabolic phenotyping method in breast cancer tissue that uses desorption electrospray ionization mass spectrometry imaging (DESI MSI), both as a novel diagnostic tool and as a method to further characterize metabolic changes in breast cancer tissue and the tumor microenvironment. In this prospective single-center study, 126 intraoperative tissue biopsies from tumor and tumor bed from 50 patients undergoing surgical resections were subject to DESI MSI. Global DESI MSI models were able to distinguish adipose, stromal, and glandular tissue based on their metabolomic fingerprint. Tumor tissue and tumor-associated stroma showed evident changes in their fatty acid and phospholipid composition compared with normal glandular and stromal tissue. Diagnosis of breast cancer was achieved with an accuracy of 98.2% based on DESI MSI data (PPV 0.96, NVP 1, specificity 0.96, sensitivity 1). In the tumor group, correlation between metabolomic profile and tumor grade/hormone receptor status was found. Overall classification accuracy was 87.7% (PPV 0.92, NPV 0.9, specificity 0.9, sensitivity 0.92). These results demonstrate that DESI MSI may be a valuable tool in the improved diagnosis of breast cancer in the future. The identified tumor-associated metabolic changes support theories of de novo lipogenesis in tumor tissue and the role of stroma tissue in tumor growth and development and overall disease prognosis. Cancer Res; 75(9); 1828-37. Ó2015 AACR.