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).
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.
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.
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.