A Stacked Multimodality Model Based on Functional MRI Features and Deep Learning Radiomics for Predicting the Early Response to Radiotherapy in Nasopharyngeal Carcinoma

IF 3.8 2区医学 Q1 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING

Academic Radiology Pub Date : 2025-03-01 DOI:10.1016/j.acra.2024.10.011

Xiaowen Wang , Jian Song , Qingtao Qiu , Ya Su , Lizhen Wang , Xiujuan Cao

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引用次数: 0

Abstract

Background

This study aimed to construct and assess a comprehensive model that integrates MRI-derived deep learning radiomics, functional imaging (fMRI), and clinical indicators to predict early efficacy of radiotherapy in nasopharyngeal carcinoma (NPC).

Methods

This retrospective study recruited NPC patients with radiotherapy from two Chinese hospitals between October 2018 and July 2022, divided into a training set (hospital I, 194 cases), an internal validation set (hospital I, 82 cases), and an external validation set (hospital II, 40 cases). We extracted 3404 radiomic features and 2048 deep learning features from multi-sequence MRI includes T1WI, CE-T1WI, T2WI and T2WI/FS. Additionally, both the Apparent diffusion coefficient (ADC), its maximum (ADCmax) and Tumor blood flow (TBF), its maximum (TBFmax) were obtained by Diffusion-weighted imaging (DWI) and Arterial spin labeling (ASL) respectively. We used four classifiers (LR, XGBoost, SVM and KNN) and stacked algorithm as model construction methods. The area under the receiver operating characteristic curve (AUC) and decision curve analysis was used to assess models.

Results

The manual radiomics model based on XGBoost and the deep learning model based on KNN (the AUCs in the training set: 0.909, 0.823, respectively) showed better predictive efficacy than other machine learning algorithms. The stacked model that integrated MRI-based deep learning radiomics, fMRI, and hematological indicators, has the strongest efficacy prediction ability of AUC in the training set [0.984 (95%CI: 0.972–0.996)], the internal validation set [0.936 (95%CI: 0.885–0.987)], and the external validation set [0.959 (95%CI: 0.901–1.000)].

Conclusion

Our research has developed a clinical-radiomics integrated model based on MRI which can predict early radiotherapy response in NPC and provide guidance for personalized treatment.

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基于功能磁共振成像特征和深度学习放射组学的堆叠多模态模型，用于预测鼻咽癌放疗的早期反应

研究背景本研究旨在构建和评估一个综合模型，该模型整合了MRI衍生的深度学习放射组学、功能成像（fMRI）和临床指标，用于预测鼻咽癌（NPC）放疗的早期疗效：这项回顾性研究招募了2018年10月至2022年7月期间在两家中国医院接受放疗的鼻咽癌患者，分为训练集（医院I，194例）、内部验证集（医院I，82例）和外部验证集（医院II，40例）。我们从多序列 MRI（包括 T1WI、CE-T1WI、T2WI 和 T2WI/FS）中提取了 3404 个放射学特征和 2048 个深度学习特征。此外，通过弥散加权成像（DWI）和动脉自旋标记（ASL）分别获得了表观弥散系数（ADC）及其最大值（ADCmax）和肿瘤血流（TBF）及其最大值（TBFmax）。我们使用了四种分类器（LR、XGBoost、SVM 和 KNN）和堆叠算法作为模型构建方法。我们使用接收者工作特征曲线下面积（AUC）和决策曲线分析来评估模型：结果：与其他机器学习算法相比，基于 XGBoost 的人工放射组学模型和基于 KNN 的深度学习模型（训练集的 AUC 分别为 0.909 和 0.823）显示出更好的预测效果。整合了基于 MRI 深度学习的放射组学、fMRI 和血液学指标的叠加模型在训练集 AUC [0.984（95%CI：0.972-0.996）]、内部验证集 [0.936（95%CI：0.885-0.987）]和外部验证集 [0.959（95%CI：0.901-1.000）]中的疗效预测能力最强：我们的研究建立了一个基于核磁共振成像的临床-放射组学综合模型，该模型可预测鼻咽癌的早期放疗反应，并为个性化治疗提供指导。

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来源期刊

Academic Radiology 医学-核医学

CiteScore

7.60

自引率

10.40%

发文量

432

审稿时长

18 days

期刊介绍： Academic Radiology publishes original reports of clinical and laboratory investigations in diagnostic imaging, the diagnostic use of radioactive isotopes, computed tomography, positron emission tomography, magnetic resonance imaging, ultrasound, digital subtraction angiography, image-guided interventions and related techniques. It also includes brief technical reports describing original observations, techniques, and instrumental developments; state-of-the-art reports on clinical issues, new technology and other topics of current medical importance; meta-analyses; scientific studies and opinions on radiologic education; and letters to the Editor.