Novel multi-task learning for Alzheimer's stage classification using hippocampal MRI segmentation, feature fusion, and nomogram modeling.

IF 3.4 3区 医学 Q2 MEDICINE, RESEARCH & EXPERIMENTAL
Wenqi Hu, Qiaohui Du, Lisi Wei, Dawei Wang, Guang Zhang
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引用次数: 0

Abstract

Objective: To develop and validate a comprehensive and interpretable framework for multi-class classification of Alzheimer's disease (AD) progression stages based on hippocampal MRI, integrating radiomic, deep, and clinical features.

Materials and methods: This retrospective multi-center study included 2956 patients across four AD stages (Non-Demented, Very Mild Demented, Mild Demented, Moderate Demented). T1-weighted MRI scans were processed through a standardized pipeline involving hippocampal segmentation using four models (U-Net, nnU-Net, Swin-UNet, MedT). Radiomic features (n = 215) were extracted using the SERA platform, and deep features (n = 256) were learned using an LSTM network with attention applied to hippocampal slices. Fused features were harmonized with ComBat and filtered by ICC (≥ 0.75), followed by LASSO-based feature selection. Classification was performed using five machine learning models, including Logistic Regression (LR), Support Vector Machine (SVM), Random Forest (RF), Multilayer Perceptron (MLP), and eXtreme Gradient Boosting (XGBoost). Model interpretability was addressed using SHAP, and a nomogram and decision curve analysis (DCA) were developed. Additionally, an end-to-end 3D CNN-LSTM model and two transformer-based benchmarks (Vision Transformer, Swin Transformer) were trained for comparative evaluation.

Results: MedT achieved the best hippocampal segmentation (Dice = 92.03% external). Fused features yielded the highest classification performance with XGBoost (external accuracy = 92.8%, AUC = 94.2%). SHAP identified MMSE, hippocampal volume, and APOE ε4 as top contributors. The nomogram accurately predicted early-stage AD with clinical utility confirmed by DCA. The end-to-end model performed acceptably (AUC = 84.0%) but lagged behind the fused pipeline. Statistical tests confirmed significant performance advantages for feature fusion and MedT-based segmentation.

Conclusions: This study demonstrates that integrating radiomics, deep learning, and clinical data from hippocampal MRI enables accurate and interpretable classification of AD stages. The proposed framework is robust, generalizable, and clinically actionable, representing a scalable solution for AD diagnostics.

基于海马MRI分割、特征融合和图建模的新型多任务学习用于阿尔茨海默氏症分期分类。
目的:开发和验证基于海马MRI的阿尔茨海默病(AD)进展分期的综合、可解释的多类别分类框架,整合放射学、深部和临床特征。材料和方法:这项回顾性多中心研究包括2956例AD患者,分为四个阶段(非痴呆、极轻度痴呆、轻度痴呆、中度痴呆)。t1加权MRI扫描通过标准化管道处理,包括使用四种模型(U-Net, nnU-Net, swan - unet, MedT)进行海马分割。使用SERA平台提取放射特征(n = 215),使用LSTM网络学习深度特征(n = 256),并将注意力应用于海马体切片。融合后的特征与ComBat进行协调,通过ICC(≥0.75)进行滤波,然后基于lasso进行特征选择。使用五种机器学习模型进行分类,包括逻辑回归(LR)、支持向量机(SVM)、随机森林(RF)、多层感知器(MLP)和极端梯度增强(XGBoost)。使用SHAP解决了模型的可解释性,并开发了nomogram和decision curve analysis (DCA)。此外,还训练了端到端3D CNN-LSTM模型和两个基于变压器的基准(Vision Transformer, Swin Transformer)进行比较评估。结果:MedT达到了最好的海马分割效果(Dice = 92.03%)。融合特征在XGBoost中获得了最高的分类性能(外部准确率= 92.8%,AUC = 94.2%)。SHAP发现MMSE、海马体积和APOE ε4是最重要的贡献者。nomogram能准确预测早期AD, DCA证实了其临床应用价值。端到端模型表现良好(AUC = 84.0%),但落后于融合管道。统计测试证实了特征融合和基于medt的分割的显著性能优势。结论:本研究表明,将放射组学、深度学习和海马MRI临床数据相结合,可以准确、可解释地分类AD的分期。所提出的框架具有鲁棒性、通用性和临床可操作性,代表了AD诊断的可扩展解决方案。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
European Journal of Medical Research
European Journal of Medical Research 医学-医学:研究与实验
CiteScore
3.20
自引率
0.00%
发文量
247
审稿时长
>12 weeks
期刊介绍: European Journal of Medical Research publishes translational and clinical research of international interest across all medical disciplines, enabling clinicians and other researchers to learn about developments and innovations within these disciplines and across the boundaries between disciplines. The journal publishes high quality research and reviews and aims to ensure that the results of all well-conducted research are published, regardless of their outcome.
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