Identifying brain tumors by differential mobility spectrometry analysis of diathermy smoke

Ilkka Haapala MD 1 , Markus Karjalainen MSc 2 , Anton Kontunen MSc 2 , Antti Vehkaoja MSc, PhD, DSc(Tech) 2 , Kristiina Nordfors MD, PhD 3 , Hannu Haapasalo MD, PhD 4 , Joonas Haapasalo MD, PhD 1 , 2 , Niku Oksala MD, PhD, DSc(Med) 2 , 5 , and Antti Roine MD, PhD 2 , 6
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  • 1 Unit of Neurosurgery, Tampere University Hospital;
  • 2 Faculty of Medicine and Health Technology, Tampere University;
  • 3 Department of Pediatrics, Tampere University Hospital;
  • 4 Fimlab Laboratories Ltd., Tampere University Hospital;
  • 5 Centre for Vascular Surgery and Interventional Radiology, Tampere University Hospital; and
  • 6 Department of Surgery, Tampere University Hospital, Hatanpää Hospital, Tampere, Finland
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OBJECTIVE

There is a need for real-time, intraoperative tissue identification technology in neurosurgery. Several solutions are under development for that purpose, but their adaptability for standard clinical use has been hindered by high cost and impracticality issues. The authors tested and preliminarily validated a method for brain tumor identification that is based on the analysis of diathermy smoke using differential mobility spectrometry (DMS).

METHODS

A DMS connected to a special smoke sampling system was used to discriminate brain tumors and control samples ex vivo in samples from 28 patients who had undergone neurosurgical operations. They included meningiomas (WHO grade I), pilocytic astrocytomas (grade I), other low-grade gliomas (grade II), glioblastomas (grade IV), CNS metastases, and hemorrhagic or traumatically damaged brain tissue as control samples. Original samples were cut into 694 smaller specimens in total.

RESULTS

An overall classification accuracy (CA) of 50% (vs 14% by chance) was achieved in 7-class classification. The CA improved significantly (up to 83%) when the samples originally preserved in Tissue-Tek conservation medium were excluded from the analysis. The CA further improved when fewer classes were used. The highest binary classification accuracy, 94%, was obtained in low-grade glioma (grade II) versus control.

CONCLUSIONS

The authors’ results show that surgical smoke from various brain tumors has distinct DMS profiles and the DMS analyzer connected to a special sampling system can differentiate between tumorous and nontumorous tissue and also between different tumor types ex vivo.

ABBREVIATIONS CA = classification accuracy; DMS = differential mobility spectrometry; GBM = glioblastoma; LDA = linear discriminant analysis; LGG = low-grade glioma; LOOCV = leave-one-out cross-validation; OCT = optical coherence tomography; REIMS = rapid evaporate ionization mass spectrometry; 10-f-CV = 10-fold cross-validation.

Supplementary Materials

    • Supplemental Figs. 1 and 2 (PDF 2.30 KB)

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Contributor Notes

Correspondence Ilkka Haapala: Tampere University Hospital, Tampere, Finland. ilkka.haapala@fimnet.fi.

INCLUDE WHEN CITING Published online June 14, 2019; DOI: 10.3171/2019.3.JNS19274.

Disclosures Mr. Karjalainen, Mr. Kontunen, Dr. Oksala, and Dr. Roine: direct stock ownership in Olfactomics Ltd. Dr. Roine: employee of Olfactomics Ltd.

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