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Mass spectrometry imaging as a tool for surgical decision-making.

ABSTRACT: Despite significant advances in image-guided therapy, surgeons are still too often left with uncertainty when deciding to remove tissue. This binary decision between removing and leaving tissue during surgery implies that the surgeon should be able to distinguish tumor from healthy tissue. In neurosurgery, current image-guidance approaches such as magnetic resonance imaging (MRI) combined with neuronavigation offer a map as to where the tumor should be, but the only definitive method to characterize the tissue at stake is histopathology. Although extremely valuable information is derived from this gold standard approach, it is limited to very few samples during surgery and is not practically used for the delineation of tumor margins. The development and implementation of faster, comprehensive, and complementary approaches for tissue characterization are required to support surgical decision-making--an incremental and iterative process with tumor removed in multiple and often minute biopsies. The development of atmospheric pressure ionization sources makes it possible to analyze tissue specimens with little to no sample preparation. Here, we highlight the value of desorption electrospray ionization as one of many available approaches for the analysis of surgical tissue. Twelve surgical samples resected from a patient during surgery were analyzed and diagnosed as glioblastoma tumor or necrotic tissue by standard histopathology, and mass spectrometry results were further correlated to histopathology for critical validation of the approach. The use of a robust statistical approach reiterated results from the qualitative detection of potential biomarkers of these tissue types. The correlation of the mass spectrometry and histopathology results to MRI brings significant insight into tumor presentation that could not only serve to guide tumor resection, but that is also worthy of more detailed studies on our understanding of tumor presentation on MRI.

SUBMITTER: Calligaris D

PROVIDER: S-EPMC3957233 | biostudies-literature | 2013 Nov

REPOSITORIES: biostudies-literature

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Mass spectrometry imaging as a tool for surgical decision-making.

Calligaris David D Norton Isaiah I Feldman Daniel R DR Ide Jennifer L JL Dunn Ian F IF Eberlin Livia S LS Cooks R Graham RG Jolesz Ferenc A FA Golby Alexandra J AJ Santagata Sandro S Agar Nathalie Y NY

Journal of mass spectrometry : JMS 20131101 11

Despite significant advances in image-guided therapy, surgeons are still too often left with uncertainty when deciding to remove tissue. This binary decision between removing and leaving tissue during surgery implies that the surgeon should be able to distinguish tumor from healthy tissue. In neurosurgery, current image-guidance approaches such as magnetic resonance imaging (MRI) combined with neuronavigation offer a map as to where the tumor should be, but the only definitive method to characte ...[more]

PMID: 24259206

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Project description:Meningiomas are the most frequent intracranial tumors. The majority is benign slow-growing tumors but they can be difficult to treat depending on their location and size. While meningiomas are well delineated on magnetic resonance imaging by their uptake of contrast, surgical limitations still present themselves from not knowing the extent of invasion of the dura matter by meningioma cells. The development of tools to characterize tumor tissue in real or near real time could prevent recurrence after tumor resection by allowing for more precise surgery, i.e. removal of tumor with preservation of healthy tissue. The development of ambient ionization mass spectrometry for molecular characterization of tissue and its implementation in the surgical decision-making workflow carry the potential to fulfill this need. Here, we present the characterization of meningioma and dura mater by desorption electrospray ionization mass spectrometry to validate the technique for the molecular assessment of surgical margins and diagnosis of meningioma from surgical tissue in real-time. Nine stereotactically resected surgical samples and three autopsy samples were analyzed by standard histopathology and mass spectrometry imaging. All samples indicated a strong correlation between results from both techniques. We then highlight the value of desorption electrospray ionization mass spectrometry for the molecular subtyping/subgrouping of meningiomas from a series of forty genetically characterized specimens. The minimal sample preparation required for desorption electrospray ionization mass spectrometry offers a distinct advantage for applications relying on real-time information such as surgical decision-making. The technology here was tested to distinguish meningioma from dura mater as an approach to precisely define surgical margins. In addition we classify meningiomas into fibroblastic and meningothelial subtypes and more notably recognize meningiomas with NF2 genetic aberrations.

Project description:BackgroundSurgical resection with microscopically negative margins remains the main curative option for pancreatic cancer; however, in practice intraoperative delineation of resection margins is challenging. Ambient mass spectrometry imaging has emerged as a powerful technique for chemical imaging and real-time diagnosis of tissue samples. We applied an approach combining desorption electrospray ionization mass spectrometry imaging (DESI-MSI) with the least absolute shrinkage and selection operator (Lasso) statistical method to diagnose pancreatic tissue sections and prospectively evaluate surgical resection margins from pancreatic cancer surgery.Methods and findingsOur methodology was developed and tested using 63 banked pancreatic cancer samples and 65 samples (tumor and specimen margins) collected prospectively during 32 pancreatectomies from February 27, 2013, to January 16, 2015. In total, mass spectra for 254,235 individual pixels were evaluated. When cross-validation was employed in the training set of samples, 98.1% agreement with histopathology was obtained. Using an independent set of samples, 98.6% agreement was achieved. We used a statistical approach to evaluate 177,727 mass spectra from samples with complex, mixed histology, achieving an agreement of 81%. The developed method showed agreement with frozen section evaluation of specimen margins in 24 of 32 surgical cases prospectively evaluated. In the remaining eight patients, margins were found to be positive by DESI-MSI/Lasso, but negative by frozen section analysis. The median overall survival after resection was only 10 mo for these eight patients as opposed to 26 mo for patients with negative margins by both techniques. This observation suggests that our method (as opposed to the standard method to date) was able to detect tumor involvement at the margin in patients who developed early recurrence. Nonetheless, a larger cohort of samples is needed to validate the findings described in this study. Careful evaluation of the long-term benefits to patients of the use of DESI-MSI for surgical margin evaluation is also needed to determine its value in clinical practice.ConclusionsOur findings provide evidence that the molecular information obtained by DESI-MSI/Lasso from pancreatic tissue samples has the potential to transform the evaluation of surgical specimens. With further development, we believe the described methodology could be routinely used for intraoperative surgical margin assessment of pancreatic cancer.

Project description:BackgroundSurgical patients incur preventable harm from cognitive and judgment errors made under time constraints and uncertainty regarding patients' diagnoses and predicted response to treatment. Decision analysis and techniques of reinforcement learning theoretically can mitigate these challenges but are poorly understood and rarely used clinically. This review seeks to promote an understanding of decision analysis and reinforcement learning by describing their use in the context of surgical decision-making.MethodsCochrane, EMBASE, and PubMed databases were searched from their inception to June 2019. Included were 41 articles about cognitive and diagnostic errors, decision-making, decision analysis, and machine-learning. The articles were assimilated into relevant categories according to Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews guidelines.ResultsRequirements for time-consuming manual data entry and crude representations of individual patients and clinical context compromise many traditional decision-support tools. Decision analysis methods for calculating probability thresholds can inform population-based recommendations that jointly consider risks, benefits, costs, and patient values but lack precision for individual patient-centered decisions. Reinforcement learning, a machine-learning method that mimics human learning, can use a large set of patient-specific input data to identify actions yielding the greatest probability of achieving a goal. This methodology follows a sequence of events with uncertain conditions, offering potential advantages for personalized, patient-centered decision-making. Clinical application would require secure integration of multiple data sources and attention to ethical considerations regarding liability for errors and individual patient preferences.ConclusionTraditional decision-support tools are ill-equipped to accommodate time constraints and uncertainty regarding diagnoses and the predicted response to treatment, both of which often impair surgical decision-making. Decision analysis and reinforcement learning have the potential to play complementary roles in delivering high-value surgical care through sound judgment and optimal decision-making.

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Mass spectrometry imaging as a tool for surgical decision-making.

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Mass spectrometry imaging as a tool for surgical decision-making.

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