Arterial spin labelling (ASL) radiomics analysis to predict IDH mutation and MGMT methylation status in gliomas Fabio M. Doniselli1,2, Riccardo Pascuzzo1, Eleonora Bruno3, Domenico Aquino1, Mattia Verri, Alberto Redolfi, Valeria Cuccarini1, Marco Moscatelli1,2, Maria Grazia Bruzzone1, Luca Maria Sconfienza2,4 Abstract Objectives: To evaluate the strength and ability of radiomics features extracted from multiple tumor subregions on MR brain images to predict MGMT promoter (MGMT) methylation status and isocitrate dehydrogenase (IDH) mutation in glioma patients through a multiparametric MRI-based radiomics model, using arterial-spin labelling (ASL) perfusion imaging. Methods: Retrospective single-institution study in a cohort of 52 glioma patients. Radiomics-based models with a minimal set of relevant features and clinical parameters were built for MGMT methylation and IDH-mutation prediction from a training cohort (31 patients) and tested on an validation cohort (13 patients). Results: Feature selection methods (Boruta, RFE and LR-EL) identified age and 3 radiomics features for MGMT prediction and 3 features for IDH prediction. For IDH prediction, SVM classifier achieved average 96.8% accuracy and 0.929 AUC during the training phase, and 84.6% accuracy and 0.60 AUC on the test set. For MGMT methylation prediction, SVM classifier achieved average 67.7% accuracy and 0.765 AUC during the training phase, and 38.5% accuracy and 0.429 AUC on the test set. Conclusions: The classification model based on both demographic (age) and radiomic ASL perfusion characteristics had the best performance in predicting the IDH mutational status of gliomas. This result suggests that the proposed method has promising efficacy in predicting IDH mutational status. We have not obtained a sufficient result trying to correlate radiomics with the MGMT mutational pattern.

NEW ADVANCES IN QUANTITATIVE RADIOLOGY: RADIOMICS IN NEURORADIOLOGY APPLIED TO PRIMARY BRAIN TUMORS USING A MACHINE LEARNING APPROACH / F.m. Doniselli ; tutor: L.M. Sconfienza ; coordinatore: M. Del Fabbro. - : . Dipartimento di Scienze Biomediche per la Salute, 2022 Jul 05. ((34. ciclo, Anno Accademico 2021.

NEW ADVANCES IN QUANTITATIVE RADIOLOGY: RADIOMICS IN NEURORADIOLOGY APPLIED TO PRIMARY BRAIN TUMORS USING A MACHINE LEARNING APPROACH

F.M. Doniselli
2022-07-05

Abstract

Arterial spin labelling (ASL) radiomics analysis to predict IDH mutation and MGMT methylation status in gliomas Fabio M. Doniselli1,2, Riccardo Pascuzzo1, Eleonora Bruno3, Domenico Aquino1, Mattia Verri, Alberto Redolfi, Valeria Cuccarini1, Marco Moscatelli1,2, Maria Grazia Bruzzone1, Luca Maria Sconfienza2,4 Abstract Objectives: To evaluate the strength and ability of radiomics features extracted from multiple tumor subregions on MR brain images to predict MGMT promoter (MGMT) methylation status and isocitrate dehydrogenase (IDH) mutation in glioma patients through a multiparametric MRI-based radiomics model, using arterial-spin labelling (ASL) perfusion imaging. Methods: Retrospective single-institution study in a cohort of 52 glioma patients. Radiomics-based models with a minimal set of relevant features and clinical parameters were built for MGMT methylation and IDH-mutation prediction from a training cohort (31 patients) and tested on an validation cohort (13 patients). Results: Feature selection methods (Boruta, RFE and LR-EL) identified age and 3 radiomics features for MGMT prediction and 3 features for IDH prediction. For IDH prediction, SVM classifier achieved average 96.8% accuracy and 0.929 AUC during the training phase, and 84.6% accuracy and 0.60 AUC on the test set. For MGMT methylation prediction, SVM classifier achieved average 67.7% accuracy and 0.765 AUC during the training phase, and 38.5% accuracy and 0.429 AUC on the test set. Conclusions: The classification model based on both demographic (age) and radiomic ASL perfusion characteristics had the best performance in predicting the IDH mutational status of gliomas. This result suggests that the proposed method has promising efficacy in predicting IDH mutational status. We have not obtained a sufficient result trying to correlate radiomics with the MGMT mutational pattern.
SCONFIENZA, LUCA MARIA
DEL FABBRO, MASSIMO
glioma; radiomics; machine learning; neuroradiology; MGMT; IDH; RQS; ASL
Settore MED/36 - Diagnostica per Immagini e Radioterapia
Settore MED/37 - Neuroradiologia
NEW ADVANCES IN QUANTITATIVE RADIOLOGY: RADIOMICS IN NEURORADIOLOGY APPLIED TO PRIMARY BRAIN TUMORS USING A MACHINE LEARNING APPROACH / F.m. Doniselli ; tutor: L.M. Sconfienza ; coordinatore: M. Del Fabbro. - : . Dipartimento di Scienze Biomediche per la Salute, 2022 Jul 05. ((34. ciclo, Anno Accademico 2021.
Doctoral Thesis
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/2434/932853
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