Navigated tissue characterization during skin cancer surgery

Janssen, Natasja; Kaufmann, Martin; Santilli, Alice; Jamzad, Amoon; Vanderbeck, Kaitlin; Ren, Kevin; Ungi, Tamás; Mousavi, Parvin; Rudan, John F.; McKay, Douglas R.; Wang, Ami; Fichtinger, Gabor

doi:10.1007/s11548-020-02200-4

Cited by 10 publications

(10 citation statements)

References 20 publications

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“…This would likely require the use of a mass range similar to the one used in this study, since energy-based surgical techniques would not conserve the protein structure under ambient conditions. This could effectively combine diagnosis, delineation, and tumor removal into one 21 .…”

Section: Discussionmentioning

confidence: 99%

Cutaneous squamous cell carcinoma characterized by MALDI mass spectrometry imaging in combination with machine learning

Brorsen,

McKenzie,

Tullin

et al. 2024

Sci Rep

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Cutaneous squamous cell carcinoma (SCC) is an increasingly prevalent global health concern. Current diagnostic and surgical methods are reliable, but they require considerable resources and do not provide metabolomic insight. Matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI-MSI) enables detailed, spatially resolved metabolomic analysis of tissue samples. Integrated with machine learning, MALDI-MSI could yield detailed information pertaining to the metabolic alterations characteristic for SCC. These insights have the potential to enhance SCC diagnosis and therapy, improving patient outcomes while tackling the growing disease burden. This study employs MALDI-MSI data, labelled according to histology, to train a supervised machine learning model (logistic regression) for the recognition and delineation of SCC. The model, based on data acquired from discrete tumor sections (n = 25) from a mouse model of SCC, achieved a predictive accuracy of 92.3% during cross-validation on the labelled data. A pathologist unacquainted with the dataset and tasked with evaluating the predictive power of the model in the unlabelled regions, agreed with the model prediction for over 99% of the tissue areas. These findings highlight the potential value of integrating MALDI-MSI with machine learning to characterize and delineate SCC, suggesting a promising direction for the advancement of mass spectrometry techniques in the clinical diagnosis of SCC and related keratinocyte carcinomas.

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Section: Discussionmentioning

confidence: 99%

Cutaneous squamous cell carcinoma characterized by MALDI mass spectrometry imaging in combination with machine learning

Brorsen,

McKenzie,

Tullin

et al. 2024

Sci Rep

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“…Our methodology using GoPros and timestamps limited the investigation. A three‐dimentional intraoperative guidance would certainly benefit to correlate margin orientation and REIMS in vivo profiling 15,16 . The translation of ex vivo to in vivo REIMS profiling would also benefit from deeper research investigation.…”

Section: Discussionmentioning

confidence: 99%

“…The correlation ex vivo/in vivo assisted by intraoperative guidance is an essential cornerstone toward significant applicability. Development of portable mass spectrometers and improved methodology with navigation systems for in vivo lipid profiling during surgery are among key steps to enable a technology such as REIMS to improve surgical precision 15,18 . The application to sarcoma surgery may present the particularity to require substantial collaborations between centers to establish libraries of lipid profiles to face their rareness and heterogeneity.…”

Section: Discussionmentioning

confidence: 99%

Lipid profiling of electrosurgical vapors for real‐time assistance of soft tissue sarcoma resection

Vaysse,

van den Hout,

Engelen

et al. 2023

Journal of Surgical Oncology

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BackgroundSoft tissue sarcomas (STS) constitute a heterogeneous group of rare tumor entities. Treatment relies on challenging patient‐tailored surgical resection. Real‐time intraoperative lipid profiling of electrosurgical vapors by rapid evaporative ionization mass spectrometry (REIMS) may aid in achieving successful surgical R0 resection (i.e., microscopically negative‐tumor margin resection). Here, we evaluate the ex vivo accuracy of REIMS to discriminate and identify various STS from normal surrounding tissue.MethodsTwenty‐seven patients undergoing surgery for STS at Maastricht University Medical Center+ were included in the study. Samples of resected STS specimens were collected and analyzed ex vivo using REIMS. Electrosurgical cauterization of tumor and surrounding was generated successively in both cut and coagulation modes. Resected specimens were subsequently processed for gold standard histopathological review. Multivariate statistical analysis (principal component analysis‐linear discriminant analysis) and leave‐one patient‐out cross‐validation were employed to compare the classifications predicted by REIMS lipid profiles to the pathology classifications. Electrosurgical vapors produced during sarcoma resection were analyzed in vivo using REIMS.ResultsIn total, 1200 histopathologically‐validated ex vivo REIMS lipid profiles were generated from 27 patients. Ex vivo REIMS lipid profiles classified STS and normal tissues with 95.5% accuracy. STS, adipose and muscle tissues were classified with 98.3% accuracy. Well‐differentiated liposarcomas and adipose tissues could not be discriminated based on their respective lipid profiles. Distinction of leiomyosarcomas from other STS could be achieved with 96.6% accuracy. In vivo REIMS analyses generated intense mass spectrometric signals.ConclusionLipid profiling by REIMS is able to discriminate and identify STS with high accuracy and therefore constitutes a potential asset to improve surgical resection of STS in the future.

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“…In the latter case, the intraoperative definition of tumor margins is crucial in determining patient outcomes, ensuring progression-free survival, and avoiding post-operative deficits. Within this context, most clinical REIMS applications regard the analysis of breast [1,9,42,44,51], gynecological [11,13,43,52], skin [8,10,14,51], and gastrointestinal tumors [12,40,41,47,[53][54][55][56], which are quite common and frequently subjected to surgical resection. On the other hand, the building of a large database represents a challenge for brain tumors, due to the special attention paid to the preservation of healthy tissue during surgery.…”

Section: Cancer Diagnosismentioning

confidence: 99%

“…Since electrocautery is normally employed in surgery, REIMS thas been used in principle in the clinical field for diagnostic purposes for the analysis of biological samples and the differentiation between healthy cells and cancer cells [7][8][9][10][11][12][13][14]. It has immediately raised a lot of clamor for the possibility of identifying the tissues in real-time, posing itself as an alternative to the longer and more laborious techniques of histopathology, or to temporary histology that showed a high probability of error.…”

Section: Introductionmentioning

confidence: 99%

Rapid evaporative ionization mass spectrometry: A survey through 15 years of applications

Cafarella,

Mangraviti,

Rigano

et al. 2024

J of Separation Science

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Rapid evaporative ionization mass spectrometry (REIMS) is a relatively recent MS technique explored in many application fields, demonstrating high versatility in the detection of a wide range of chemicals, from small molecules (phenols, amino acids, di‐ and tripeptides, organic acids, and sugars) to larger biomolecules, that is, phospholipids and triacylglycerols. Different sampling devices were used depending on the analyzed matrix (liquid or solid), resulting in distinct performances in terms of automation, reproducibility, and sensitivity. The absence of laborious and time‐consuming sample preparation procedures and chromatographic separations was highlighted as a major advantage compared to chromatographic methods. REIMS was successfully used to achieve a comprehensive sample profiling according to a metabolomics untargeted analysis. Moreover, when a multitude of samples were available, the combination with chemometrics allowed rapid sample differentiation and the identification of discriminant features. The present review aims to provide a survey of literature reports based on the use of such analytical technology, highlighting its mode of operation in different application areas, ranging from clinical research, mostly focused on cancer diagnosis for the accurate identification of tumor margins, to the agri‐food sector aiming at the safeguard of food quality and security.

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Navigated tissue characterization during skin cancer surgery

Cited by 10 publications

References 20 publications

Cutaneous squamous cell carcinoma characterized by MALDI mass spectrometry imaging in combination with machine learning

Cutaneous squamous cell carcinoma characterized by MALDI mass spectrometry imaging in combination with machine learning

Lipid profiling of electrosurgical vapors for real‐time assistance of soft tissue sarcoma resection

Rapid evaporative ionization mass spectrometry: A survey through 15 years of applications

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