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. 2024 Apr 30:14:1352509.
doi: 10.3389/fonc.2024.1352509. eCollection 2024.

Rapid identification of pediatric brain tumors with differential mobility spectrometry

Ilkka Haapala #  1   2   3 , Anton Rauhameri #  2   3 , Meri Mäkelä  3   4 , Markus Karjalainen  3   4 , Anton Kontunen  2   3   4 , Markus Mieskolainen  1   3 , Hannu Haapasalo  3   5 , Antti Roine  3   4 , Niku Oksala  2   3   4 , Antti Vehkaoja  2   3 , Joonas Haapasalo  1   2   3 , Kristiina Nordfors  2   3   6
Affiliations

Rapid identification of pediatric brain tumors with differential mobility spectrometry

Ilkka Haapala et al. Front Oncol. .

Abstract

Introduction: Brain tumors are a major source of disease burden in pediatric population, with the most common tumor types being pilocytic astrocytoma, ependymoma and medulloblastoma. In every tumor entity, surgery is the cornerstone of treatment, but the importance of gross-total resection and the corresponding patient prognosis is highly variant. However, real-time identification of pediatric CNS malignancies based on the histology of the frozen sections alone is especially troublesome. We propose a novel method based on differential mobility spectrometry (DMS) analysis for rapid identification of pediatric brain tumors.

Methods: We prospectively obtained tumor samples from 15 pediatric patients (5 pilocytic astrocytomas, 5 ependymomas and 5 medulloblastomas). The samples were cut into 36 smaller specimens that were analyzed with the DMS.

Results: With linear discriminant analysis algorithm, a classification accuracy (CA) of 70% was reached. Additionally, a 75% CA was achieved in a pooled analysis of medulloblastoma vs. gliomas.

Discussion: Our results show that the DMS is able to differentiate most common pediatric brain tumor samples, thus making it a promising additional instrument for real-time brain tumor diagnostics.

Keywords: differential mobility spectrometry; neuro-oncology; pediatric brain tumor; pediatric neuro-oncology; rapid diagnostics.

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Conflict of interest statement

Authors NO, AR, AK, and MK are shareholders of the company Olfactomics Ltd. MMä is an employee of the company Olfactomics Ltd. HH was employed by the company Fimlab Laboratories Ltd. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

Figure 1
Figure 1
The experiment workflow.
Figure 2
Figure 2
The setup for DMS analysis: (A) Laser sampling unit (ATLAS). A custom-built metallic box in which the computer-controlled laser nozzle for electrocoagulation and a suction tube for evacuating the resulting smoke moves above the well plate (B) Enlargement of the laser nozzle and suction tube inside the sampling unit (C) Signs of laser coagulation in tumor specimens after sampling (D) DMS analyzer (IonVision) where the smoke from ATLAS is ionized and driven into the sensor.
Figure 3
Figure 3
The dispersion spectra. Top row: the average dispersion spectra of each class calculated pixel wise. Middle row: the variation in the dispersion spectra within each class. Bottom row: the differences in the spectra between classes. The red color indicates greater values for the former and blue color for the latter tumor group in the given binary classification (for example in the bottom left comparison: former – medulloblastoma, latter – ependymoma).
Figure 4
Figure 4
ROC -curve of the binary classification (medulloblastoma vs. gliomas).
See this image and copyright information in PMC

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