Hyppää sisältöön
    • Suomeksi
    • In English
Trepo
  • Suomeksi
  • In English
  • Kirjaudu
Näytä viite 
  •   Etusivu
  • Trepo
  • TUNICRIS-julkaisut
  • Näytä viite
  •   Etusivu
  • Trepo
  • TUNICRIS-julkaisut
  • Näytä viite
JavaScript is disabled for your browser. Some features of this site may not work without it.

Method for the Intraoperative Detection of IDH Mutation in Gliomas with Differential Mobility Spectrometry

Haapala, Ilkka; Rauhameri, Anton; Roine, Antti; Mäkelä, Meri; Kontunen, Anton; Karjalainen, Markus; Laakso, Aki; Koroknay-Pál, Päivi; Nordfors, Kristiina; Haapasalo, Hannu; Oksala, Niku; Vehkaoja, Antti; Haapasalo, Joonas (2022-05)

 
Avaa tiedosto
curroncol_29_00265_v2.pdf (508.5Kt)
Lataukset: 



Haapala, Ilkka
Rauhameri, Anton
Roine, Antti
Mäkelä, Meri
Kontunen, Anton
Karjalainen, Markus
Laakso, Aki
Koroknay-Pál, Päivi
Nordfors, Kristiina
Haapasalo, Hannu
Oksala, Niku
Vehkaoja, Antti
Haapasalo, Joonas
05 / 2022

CURRENT ONCOLOGY
doi:10.3390/curroncol29050265
Näytä kaikki kuvailutiedot
Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi:tuni-202206015407

Kuvaus

Peer reviewed
Tiivistelmä
<p>Isocitrate dehydrogenase (IDH) mutation status is an important factor for surgical decision-making: patients with IDH-mutated tumors are more likely to have a good long-term prognosis, and thus favor aggressive resection with more survival benefit to gain. Patients with IDH wild-type tumors have generally poorer prognosis and, therefore, conservative resection to avoid neurological deficit is favored. Current histopathological analysis with frozen sections is unable to identify IDH mutation status intraoperatively, and more advanced methods are therefore needed. We examined a novel method suitable for intraoperative IDH mutation identification that is based on the differential mobility spectrometry (DMS) analysis of the tumor. We prospectively obtained tumor samples from 22 patients, including 11 IDH-mutated and 11 IDH wild-type tumors. The tumors were cut in 88 smaller specimens that were analyzed with DMS. With a linear discriminant analysis (LDA) algorithm, the DMS was able to classify tumor samples with 86% classification accuracy, 86% sensitivity, and 85% specificity. Our results show that DMS is able to differentiate IDH-mutated and IDH wild-type tumors with good accuracy in a setting suitable for intraoperative use, which makes it a promising novel solution for neurosurgical practice.</p>
Kokoelmat
  • TUNICRIS-julkaisut [20173]
Kalevantie 5
PL 617
33014 Tampereen yliopisto
oa[@]tuni.fi | Tietosuoja | Saavutettavuusseloste
 

 

Selaa kokoelmaa

TekijätNimekkeetTiedekunta (2019 -)Tiedekunta (- 2018)Tutkinto-ohjelmat ja opintosuunnatAvainsanatJulkaisuajatKokoelmat

Omat tiedot

Kirjaudu sisäänRekisteröidy
Kalevantie 5
PL 617
33014 Tampereen yliopisto
oa[@]tuni.fi | Tietosuoja | Saavutettavuusseloste