用于同时预测胶质瘤中IDH突变和Ki-67表达的多模态ConvNeXt Tiny深度学习模型
A multimodal ConvNeXt-Tiny deep learning model for simultaneous prediction of IDH mutation and Ki-67 expression in gliomas.
摘要(中文译文)
构建并验证基于ConvNeXt-Tiny的多任务深度学习模型，用于同步预测胶质瘤中异柠檬酸脱氢酶（IDH）突变状态和Ki-67表达水平。本回顾性多中心研究纳入来自两家医院的成人弥漫性胶质瘤患者，分别作为训练集和独立验证集。所有患者均在术前4周内接受多模态MRI检查，包括T2加权成像（T2WI）、T2液体衰减反转恢复序列（T2-FLAIR）、对比增强T1加权成像（T1CE）和三维动脉自旋标记成像（ASL）。对MRI数据进行统一预处理以提取传统影像组学特征，并构建基于ConvNeXt-Tiny的双任务深度学习模型。将临床特征、影像组学特征和深度学习特征进行多模态融合以建立多模态模型，并与其他预测模型进行比较。采用受试者工作特征（ROC）曲线、校准曲线、决策曲线分析和净重分类指数（NRI）评估模型性能和临床实用性。应用SHapley加法解释（SHAP）分析和梯度加权类激活映射（Grad-CAM）对模型决策进行可视化解释。多模态模型在IDH突变状态和Ki-67表达水平预测方面均表现出最佳的诊断性能。对于IDH预测，多模态模型在训练集和测试集中的AUC分别为0.901和0.883；对于Ki-67预测，曲线下面积（AUC）分别达到0.939和0.924。NRI分析进一步证实多模态模型在两项任务中均显著改善了病例重分类。校准曲线和Hosmer-Lemeshow检验表明模型拟合良好，决策曲线分析显示更高的净临床获益。SHAP分析揭示模型预测主要依赖深度学习特征，尤其是源自T1CE、ADC和脑血流量（CBF）图像的特征，而影像组学特征和临床变量的贡献较小。Grad-CAM可视化显示模型主要关注肿瘤相关区域。本研究开发了一种基于ConvNeXt-Tiny的多任务模型，能够术前同步预测成人弥漫性胶质瘤的IDH突变和Ki-67状态，并展现出优于单任务模型和传统模型的稳健性能。
Abstract
To construct and validate a multi-task deep learning model based on ConvNeXt-Tiny for synchronous prediction of isocitrate dehydrogenase (IDH) mutation status and Ki-67 expression level in gliomas. This retrospective multicenter study included adult patients with diffuse glioma from two hospitals, which served as the training set and the independent validation set, respectively. All patients underwent multimodal MRI examinations within 4 weeks before surgery, including T2-weighted imaging (T2WI), T2-fluid attenuated inversion recovery (T2-FLAIR), contrast-enhanced T1-weighted imaging (T1CE), and three-dimensional arterial spin labeling imaging (ASL). MRI data were uniformly preprocessed to extract traditional radiomics features, and a dual-task deep learning model based on ConvNeXt-Tiny was constructed. Clinical characteristics, radiomics features, and deep learning features were multimodally fused to establish the multimodal model and were compared with other prediction models. Model performance and clinical utility were evaluated using receiver operating characteristic (ROC) curves, calibration curves, decision curve analysis, and the net reclassification index (NRI). SHapley additive explanations (SHAP) analysis and gradient-weighted class activation mapping (Grad-CAM) were applied for visual interpretation of model decisions. The multimodal model demonstrated the best diagnostic performance for both IDH mutation status and Ki-67 expression level prediction. For IDH prediction, the multimodal model achieved AUCs of 0.901 and 0.883 in the training set and test set, respectively, while for Ki-67 prediction, the area under the curves (AUCs) reached 0.939 and 0.924. NRI analysis further confirmed that the multimodal model significantly improved case reclassification in both tasks. Calibration curves and the Hosmer-Lemeshow test indicated good model fit, and decision curve analysis showed a higher net clinical benefit. SHAP analysis revealed that model predictions mainly relied on deep learning features, particularly those derived from T1CE, ADC, and cerebral blood flow (CBF) images, whereas radiomics features and clinical variables contributed less. Grad-CAM visualizations demonstrated that the model primarily focused on tumor-related regions. This study developed a ConvNeXt-Tiny-based multi-task model that enables preoperative synchronous prediction of IDH mutation and Ki-67 status in adult diffuse glioma and exhibits robust performance superior to single-task and traditional models.
如何引用
AMA
Juan Du, Linsha Yang, Duo Zhang, Jinguo Wang, Shuo Wu, Defeng Liu, et al. A multimodal ConvNeXt-Tiny deep learning model for simultaneous prediction of IDH mutation and Ki-67 expression in gliomas.. PloS one. 2026; doi:10.1371/journal.pone.0351757.
APA
Juan Du, Linsha Yang, Duo Zhang, Jinguo Wang, Shuo Wu, Defeng Liu, et al (2026). A multimodal ConvNeXt-Tiny deep learning model for simultaneous prediction of IDH mutation and Ki-67 expression in gliomas.. PloS one. https://doi.org/10.1371/journal.pone.0351757
GB/T 7714
Juan Du, Linsha Yang, Duo Zhang, Jinguo Wang, Shuo Wu, Defeng Liu, et al. A multimodal ConvNeXt-Tiny deep learning model for simultaneous prediction of IDH mutation and Ki-67 expression in gliomas.[J]. PloS one, 2026 doi:10.1371/journal.pone.0351757.
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