Development of A Radiomic Model for MGMT Promoter Methylation Detection in Glioblastoma Using Conventional MRI

被引:2
作者
Doniselli, Fabio M. [1 ,2 ]
Pascuzzo, Riccardo [1 ]
Agro, Massimiliano [3 ]
Aquino, Domenico [1 ]
Anghileri, Elena [4 ]
Farinotti, Mariangela [5 ]
Pollo, Bianca [6 ]
Paterra, Rosina [4 ]
Cuccarini, Valeria [1 ]
Moscatelli, Marco [1 ,2 ]
Dimeco, Francesco [7 ,8 ,9 ]
Sconfienza, Luca Maria [2 ,10 ]
机构
[1] Fdn IRCCS Ist Neurol Carlo Besta, Neuroradiol Unit, I-20133 Milan, Italy
[2] Univ Milan, Dept Biomed Sci Hlth, I-20133 Milan, Italy
[3] Univ Milan, Postgrad Sch Radiodiagnost, I-20122 Milan, Italy
[4] Fdn IRCCS Ist Neurol Carlo Besta, Neurooncol Unit, I-20133 Milan, Italy
[5] Fdn IRCCS Ist Neurol Carlo Besta, Neuroepidemiol Unit, I-20133 Milan, Italy
[6] Fdn IRCCS Ist Neurol Carlo Besta, Neuropathol Unit, I-20133 Milan, Italy
[7] Fdn IRCCS Ist Neurol Carlo Besta, Dept Neurosurg, I-20133 Milan, Italy
[8] Univ Milan, Dept Oncol & Hematol Oncol, I-20122 Milan, Italy
[9] Johns Hopkins Med Sch, Dept Neurol Surg, Baltimore, MD 21205 USA
[10] IRCCS Ist Ortoped Galeazzi, Radiol Unit, I-20161 Milan, Italy
关键词
radiomics; MGMT promoter methylation; glioblastoma; neuro-oncology; SURVIVAL; METHYLTRANSFERASE; TEMOZOLOMIDE; PREDICTION; SIGNATURE; DIFFUSION; BIOMARKER; FEATURES; GLIOMAS; IMAGES;
D O I
10.3390/ijms25010138
中图分类号
Q5 [生物化学]; Q7 [分子生物学];
学科分类号
071010 ; 081704 ;
摘要
The methylation of the O6-methylguanine-DNA methyltransferase (MGMT) promoter is a molecular marker associated with a better response to chemotherapy in patients with glioblastoma (GB). Standard pre-operative magnetic resonance imaging (MRI) analysis is not adequate to detect MGMT promoter methylation. This study aims to evaluate whether the radiomic features extracted from multiple tumor subregions using multiparametric MRI can predict MGMT promoter methylation status in GB patients. This retrospective single-institution study included a cohort of 277 GB patients whose 3D post-contrast T1-weighted images and 3D fluid-attenuated inversion recovery (FLAIR) images were acquired using two MRI scanners. Three separate regions of interest (ROIs) showing tumor enhancement, necrosis, and FLAIR hyperintensities were manually segmented for each patient. Two machine learning algorithms (support vector machine (SVM) and random forest) were built for MGMT promoter methylation prediction from a training cohort (196 patients) and tested on a separate validation cohort (81 patients), based on a set of automatically selected radiomic features, with and without demographic variables (i.e., patients' age and sex). In the training set, SVM based on the selected radiomic features of the three separate ROIs achieved the best performances, with an average of 83.0% (standard deviation: 5.7%) for accuracy and 0.894 (0.056) for the area under the curve (AUC) computed through cross-validation. In the test set, all classification performances dropped: the best was obtained by SVM based on the selected features extracted from the whole tumor lesion constructed by merging the three ROIs, with 64.2% (95% confidence interval: 52.8-74.6%) accuracy and 0.572 (0.439-0.705) for AUC. The performances did not change when the patients' age and sex were included with the radiomic features into the models. Our study confirms the presence of a subtle association between imaging characteristics and MGMT promoter methylation status. However, further verification of the strength of this association is needed, as the low diagnostic performance obtained in this validation cohort is not sufficiently robust to allow clinically meaningful predictions.
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页数:15
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