In a recent BJC | British Journal of Cancer paper, researchers conducted a comprehensive multi-center study on the use of rapid evaporative ionization mass spectrometry (REIMS) for classifying histological tissues and diagnosing diseases. They evaluated REIMS' effectiveness in detecting breast tumors across multiple clinics, showcasing its potential for real-time tissue classification during breast cancer surgeries.
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REIMS Technology in Surgery
Breast cancer surgeries require precise identification of cancerous and non-cancerous tissues during the operation. Surgeons usually rely on postoperative histopathology to confirm clear margins, which can take weeks.
REIMS offers a solution by classifying tissues in real time during surgery. Previous studies have shown its ability to distinguish cancerous tissues from normal ones in various cancers, including breast cancer, with the potential to improve surgical outcomes by enabling immediate margin assessment.
REIMS combines electrosurgery with mass spectrometry, analyzing the aerosol produced during electrocauterization to provide instant feedback on tissue composition. It has shown promise in identifying cancerous tissues in the breast, colon, and ovaries.
Integrating REIMS into surgical workflows could reduce the need for repeat surgeries, improving patient outcomes. However, challenges like equipment differences and tissue variability have raised concerns about its reliability across different clinical settings.
Multi-Site Evaluation of REIMS in Breast Cancer Surgery
This multi-site study evaluated the performance of REIMS in clinical settings across three centers: Imperial College London, Maastricht University Medical Center (MUMC+), and Queen’s University, Kingston. Each site used the same REIMS setups to analyze breast tissues from patients undergoing surgery for invasive breast cancer.
The authors developed and tested tissue classification models, comparing results across the sites to evaluate REIMS' reliability for clinical use. They analyzed samples from 21 patients using standard tissue handling and REIMS protocols, focusing on classifying invasive ductal and lobular carcinomas and detecting molecular subtypes like Phosphatidylinositol-4,5-Bisphosphate 3-Kinase Catalytic Subunit Alpha (PIK3CA) mutations commonly found in breast cancer.
Research Methodology
The study aimed to determine whether REIMS could consistently classify breast tissues across multiple clinics. To achieve this, the researchers developed classification models based on histopathology-validated REIMS mass spectra and tested their accuracy.
Tissue samples, including both cancerous and normal tissues, were analyzed using electrosurgical generators in cutting and coagulation modes. REIMS data was collected from both normal breast fat and cancerous tissue to identify distinctive lipid profiles that could serve as biomarkers for breast cancer.
To minimize variability between clinics, standardized protocols, harmonized instrument settings, and non-clinical reference samples were used. These measures helped reduce differences in tissue handling and equipment performance across the centers.
Key Findings
The study showed that REIMS could effectively distinguish between normal and cancerous breast tissues across all clinics. The classification models achieved up to 98.6 % accuracy in cross-validation tests, demonstrating strong potential for clinical use. Even models trained at one clinical site performed well with data from another, highlighting REIMS' reliability for multi-site diagnostics.
A key outcome was the identification of distinct lipid profiles in breast cancer tissues, particularly related to changes in fatty acid metabolism linked to PIK3CA mutations. REIMS detected significant differences in specific fatty acids and phospholipids levels between normal and cancerous tissues. These biomarkers could provide insights into tumor biology and help guide treatment decisions. For example, tissues with PIK3CA mutations had higher levels of arachidonic acid metabolism, a pathway associated with cancer progression.
The authors emphasized the reproducibility of REIMS data across different sites despite minor variations in signal intensity and spectral quality caused by differences in tissue handling and electrosurgical modes. Importantly, intra-site variability was low, and the use of quality control materials ensured consistent data collection across all clinical sites.
Clinical Implications and Future Directions
This research has significant implications for breast cancer surgery and diagnostics. REIMS could become a valuable tool for classifying tissues during surgery, helping surgeons make real-time decisions about tissue margins. By providing immediate feedback on whether a margin is cancerous, REIMS could reduce the need for repetitive surgeries and ultimately improve patient outcomes.
Additionally, REIMS' potential for molecular diagnostics, such as identifying metabolic biomarkers associated with cancer subtypes, was highlighted. Detecting molecular signatures like PIK3CA mutations during surgery could enable personalized cancer treatment and more informed decisions.
Conclusion and Future Scope
REIMS demonstrated high accuracy and consistency for tissue classification and molecular diagnostics in breast cancer surgeries across multiple clinical sites. Its integration into surgical workflows for real-time analysis could significantly enhance intraoperative diagnostics, potentially reducing the need for additional surgeries and improving patient outcomes.
Future research should explore REIMS' ability to identify other cancer subtypes and its integration with imaging technologies for more precise surgical guidance. Expanding studies to larger, more diverse patient groups could provide deeper insights into REIMS' clinical utility.
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Journal Reference
Kaufmann, M., et al. (2024). Testing of rapid evaporative mass spectrometry for histological tissue classification and molecular diagnostics in a multi-site study. Br J Cancer. DOI: 10.1038/s41416-024-02739-y, https://www.nature.com/articles/s41416-024-02739-y
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