文章：

多模态磁共振成像深度学习预测甲状腺乳头状癌中央区淋巴结转移

Multimodal MRI Deep Learning for Predicting Central Lymph Node Metastasis in Papillary Thyroid Cancer

原文发布日期：2 December 2024

DOI: 10.3390/cancers16234042

类型: Article

开放获取: 是

英文摘要：

Background: Central lymph node metastasis (CLNM) in papillary thyroid cancer (PTC) significantly influences surgical decision-making strategies. Objectives: This study aims to develop a predictive model for CLNM in PTC patients using magnetic resonance imaging (MRI) and clinicopathological data. Methods: By incorporating deep learning (DL) algorithms, the model seeks to address the challenges in diagnosing CLNM and reduce overtreatment. The results were compared with traditional machine learning (ML) models. In this retrospective study, preoperative MRI data from 105 PTC patients were divided into training and testing sets. A radiologist manually outlined the region of interest (ROI) on MRI images. Three classic ML algorithms (support vector machine [SVM], logistic regression [LR], and random forest [RF]) were employed across different data modalities. Additionally, an AMMCNet utilizing convolutional neural networks (CNNs) was proposed to develop DL models for CLNM. Predictive performance was evaluated using receiver operator characteristic (ROC) curve analysis, and clinical utility was assessed through decision curve analysis (DCA). Results: Lesion diameter was identified as an independent risk factor for CLNM. Among ML models, the RF-(T1WI + T2WI, T1WI + T2WI + Clinical) models achieved the highest area under the curve (AUC) at 0.863. The DL fusion model surpassed all ML fusion models with an AUC of 0.891. Conclusions: A fusion model based on the AMMCNet architecture using MRI images and clinicopathological data was developed, effectively predicting CLNM in PTC patients.

摘要翻译： 

背景：甲状腺乳头状癌（PTC）的中央区淋巴结转移（CLNM）显著影响手术决策策略。目的：本研究旨在利用磁共振成像（MRI）和临床病理数据，构建预测PTC患者CLNM的模型。方法：通过引入深度学习（DL）算法，该模型致力于解决CLNM诊断中的挑战并减少过度治疗。研究结果与传统机器学习（ML）模型进行了比较。在这项回顾性研究中，105例PTC患者的术前MRI数据被分为训练集和测试集。由一名放射科医师在MRI图像上手动勾画感兴趣区域（ROI）。研究采用了三种经典ML算法（支持向量机[SVM]、逻辑回归[LR]和随机森林[RF]）应用于不同数据模态。此外，提出了一种利用卷积神经网络（CNN）的AMMCNet来开发用于CLNM的DL模型。通过受试者工作特征（ROC）曲线分析评估预测性能，并通过决策曲线分析（DCA）评估临床效用。结果：病灶直径被确定为CLNM的独立危险因素。在ML模型中，RF-(T1WI + T2WI, T1WI + T2WI + 临床)模型获得了最高的曲线下面积（AUC），为0.863。DL融合模型以0.891的AUC超越了所有ML融合模型。结论：本研究开发了一种基于AMMCNet架构、融合MRI图像和临床病理数据的模型，能有效预测PTC患者的CLNM。

原文链接：

Multimodal MRI Deep Learning for Predicting Central Lymph Node Metastasis in Papillary Thyroid Cancer