为了预测异柠檬酸脱氢酶（IDH）突变星形胶质瘤中周期依赖性激酶抑制因子2A/B（CDKN2A/B）纯合缺失状态，我们开发了一种基于高精度MRI的深度学习方法。分别从癌症影像存档（TCIA）和癌症基因组图谱（TCGA）中获得了234名受试者（CDKN2A/B纯合缺失阳性111人，CDKN2A/B纯合缺失阴性123人）的多参数脑部MRI数据和相应的基因组信息。在ResNet和ConvNeXt网络的基础上结合注意力机制，构建了两个独立的多序列网络（ResFN-Net和FN-Net），用于使用包括增强的T1加权成像（CE-T1WI）和T2加权成像（T2WI）的MR图像对CDKN2A/B纯合缺失状态进行分类。网络的性能由三重交叉验证总结；还进行了ROC分析。ResFN-Net的平均交叉验证准确度（ACC）为0.813。ResFN-Net的平均交叉验证曲线下面积（AUC）为0.8804。FN-Net的平均交叉验证ACC和AUC分别为0.9236和0.9704。对两个网络（ResFN-Net和FN-Net）的所有序列组合进行比较，CE-T1WI和T2WI的序列组合表现最佳，ACC和AUC分别为0.8244、0.8975和0.8971、0.9574。基于ConvNeXt网络的FN-Net深度学习网络在预测IDH突变星形胶质瘤的CDKN2A/B纯合缺失状态方面取得了有希望的性能。我们开发了一种基于术前MRI的新型深度学习网络（FN-Net），用于预测CDKN2A/B纯合缺失状态。该网络具有成为非侵入性表征脑胶质瘤中CDKN2A/B的实用工具以支持个体化分类和治疗规划的潜力。-CDKN2A/B纯合缺失状态是脑胶质瘤分级和预后的重要标记。-开发了一种基于MRI的深度学习方法来预测CDKN2A/B纯合缺失状态。-基于ConvNeXt网络的预测性能优于ResNet网络。©2023年 作者，由欧洲放射学协会独家许可。

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.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.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.The FN-Net deep learning networks based on ConvNeXt network achieved promising performance for predicting CDKN2A/B homozygous deletion status of IDH-mutant astrocytoma.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.• 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.© 2023. The Author(s), under exclusive licence to European Society of Radiology.