Radiomic model for predicting mutations in the isocitrate dehydrogenase gene in glioblastomas

Oncotarget
Kevin Li-Chun HsiehChung-Ming Lo

Abstract

The present study proposed a computer-aided diagnosis system based on radiomic features extracted through magnetic resonance imaging to determine the isocitrate dehydrogenase status in glioblastomas. Magnetic resonance imaging data were obtained from 32 patients with wild-typeisocitrate dehydrogenase and 7 patients with mutant isocitrate dehydrogenase in glioblastomas. Radiomic features, namely morphological, intensity, and textural features, were extracted from the tumor area of each patient. The feature sets were evaluated using a logistic regression classifier to develop a prediction model. The accuracy of the global morphological and intensity features was 51% (20/39) and 59% (23/39), respectively. The textural features describing local patterns yielded an accuracy of 85% (33/39), which is significantly higher than that yielded by the morphological and intensity features. The agreement level (κ) between the prediction results and biopsy-proven pathology was 0.60. The proposed diagnosis system based on radiomic textural features shows promise for application in providing suggestions to radiologists for distinguishing isocitrate dehydrogenase mutations in glioblastomas.

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Citations

Oct 18, 2018·AJR. American Journal of Roentgenology·Eyal LotanYvonne W Lui
May 3, 2019·Abdominal Radiology·Zuhir BodalalRegina Beets-Tan
Sep 19, 2020·Journal of Personalized Medicine·Nguyen Quoc Khanh LeCheng-Yu Chen
May 28, 2019·AJNR. American Journal of Neuroradiology·N SoniG Bathla
May 24, 2018·Clinical Cancer Research : an Official Journal of the American Association for Cancer Research·Chia-Feng LuCheng-Yu Chen
May 1, 2021·Journal of Personalized Medicine·Luca PasquiniAlessandro Bozzao
May 8, 2021·British Journal of Cancer·Gagandeep SinghVadim Spektor
Jul 17, 2021·Korean Journal of Radiology : Official Journal of the Korean Radiological Society·Elena PakChul-Ho Sohn
Jul 15, 2020·Academic Radiology·Sonal GoreMadhura Ingalhalikar

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Methods Mentioned

BETA
exomic sequencing
biopsies
biopsy
feature extraction

Software Mentioned

SPSS

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