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Abstract

Background: Minimal technologies exist that provide tissue specific molecular information to the surgeon in real-unsupervised principal components time. Rapid evaporative ionization mass spectrometry allows near real-time characterization of tissue by mass spectrometric analysis of the smoke plume released during electrosurgical dissection. Objectives: To develop and optimize a statistical strategy for the real-time recognition of cancer margin status during surgical excision of liver metastases. Methods: Fresh tissue samples from 25 patients with liver metastasis from colorectal adenocarcinoma were collected and analyzed using monopolar diathermy coupled to ion trap mass spectrometry. At the point of rapid evaporation of tissues during electrosurgical dissection, tissue specific charged particles are formed and these ionized molecular species are transferred within the diathermy plume to the mass spectrometer using Venturi air jet pump and PTFE tubing. Intense spectral profiles are produced (m/z range: 600-900) which are associated with the structural phospholipid content of tissues and vary significantly between the distinct histological tissue types. Results: The resulting dataset was analyzed by unsupervised principal components analysis for explorative analysis of similarities/differences in molecular ion composition between samples. The maximum margin criterion analysis, a supervised dimension reduction technique, was subsequently applied to extract tissue specific discriminating molecular ion patterns. The 3-nearest neighbour classification algorithm was applied on a reduced set of discriminant features and 10-fold cross validation carried out. Discrimination of healthy and malignant tissue was possible with a sensitivity of 96.8% and cross validation demonstrated the validity of the supervised methods. Conclusion: When paired with real-time data analysis, the iKnife is a viable potential method of real-time tissue identification including the intra-operative assessment of oncological resection margins.

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/content/papers/10.5339/qfarf.2012.AESNP7
2012-10-01
2020-09-18
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http://instance.metastore.ingenta.com/content/papers/10.5339/qfarf.2012.AESNP7
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