A novel MRI-based deep learning networks combined with attention mechanism for predicting CDKN2A/B homozygous deletion status in IDH-mutant astrocytoma

• Published in: European radiology Vol. 34; no. 1; pp. 391 - 399
• Main Authors: Zhang, Liqiang, Wang, Rui, Gao, Jueni, Tang, Yi, Xu, Xinyi, Kan, Yubo, Cao, Xu, Wen, Zhipeng, Liu, Zhi, Cui, Shaoguo, Li, Yongmei
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.01.2024; Springer Nature B.V
• Subjects: Astrocytoma; Astrocytoma - diagnostic imaging; Astrocytoma - genetics; Brain Neoplasms - diagnostic imaging; Brain Neoplasms - genetics; Cancer; Cyclin-dependent kinase; Cyclin-Dependent Kinase Inhibitor p16 - genetics; Cyclin-dependent kinases; Deep Learning; Diagnostic Radiology; Gene deletion; Glioma; Glioma - genetics; Homozygote; Humans; Image contrast; Image enhancement; Imaging; Imaging Informatics and Artificial Intelligence; Internal Medicine; Interventional Radiology; Isocitrate dehydrogenase; Isocitrate Dehydrogenase - genetics; Kinases; Magnetic resonance imaging; Magnetic Resonance Imaging - methods; Medical imaging; Medicine; Medicine & Public Health; Mutants; Mutation; Networks; Neuroimaging; Neuroradiology; Nucleotide sequence; Performance prediction; Radiology; Sequence Deletion; Ultrasound; Deep learning; Brain; Magnetic resonance imaging; Genomics; Astrocytoma
• ISSN: 1432-1084; 0938-7994; 1432-1084
• DOI: 10.1007/s00330-023-09944-y

Objectives To develop a high-accuracy MRI-based deep learning method for predicting cyclin-dependent kinase inhibitor 2A/B (CDKN2A/B) homozygous deletion status in isocitrate dehydrogenase (IDH)–mutant astrocytoma. Methods Multiparametric brain MRI data and corresponding genomic information of 234 subjects (111 positives for CDKN2A/B homozygous deletion and 123 negatives for CDKN2A/B homozygous deletion) were obtained from The Cancer Imaging Archive (TCIA) and The Cancer Genome Atlas (TCGA) respectively. Two independent multi-sequence networks (ResFN-Net and FN-Net) are built on the basis of ResNet and ConvNeXt network combined with attention mechanism to classify CDKN2A/B homozygous deletion status using MR images including contrast-enhanced T1-weighted imaging (CE-T1WI) and T2-weighted imaging (T2WI). The performance of the network is summarized by three-way cross-validation; ROC analysis is also performed. Results The average cross-validation accuracy (ACC) of ResFN-Net is 0.813. The average cross-validation area under curve (AUC) of ResFN-Net is 0.8804. The average cross-validation ACC and AUC of FN-Net is 0.9236 and 0.9704, respectively. Comparing all sequence combinations of the two networks (ResFN-Net and FN-Net), the sequence combination of CE-T1WI and T2WI performed the best, and the ACC and AUC were 0.8244, 0.8975 and 0.8971, 0.9574, respectively. Conclusions The FN-Net deep learning networks based on ConvNeXt network achieved promising performance for predicting CDKN2A/B homozygous deletion status of IDH-mutant astrocytoma. Clinical relevance statement A novel deep learning network (FN-Net) based on preoperative MRI was developed to predict the CDKN2A/B homozygous deletion status. This network has the potential to be a practical tool for the noninvasive characterization of CDKN2A/B in glioma to support personalized classification and treatment planning. Key Points • CDKN2A/B homozygous deletion status is an important marker for glioma grading and prognosis. • An MRI-based deep learning approach was developed to predict CDKN2A/B homozygous deletion status. • The predictive performance based on ConvNeXt network was better than that of ResNet network.