基于机器学习的多参数磁共振成像放射组学预测宫颈癌淋巴结转移。

IF 1.1 4区 医学 Q3 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING
Jing Liu, Mingxuan Zhu, Li Li, Lele Zang, Lan Luo, Fei Zhu, Huiqi Zhang, Qin Xu
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引用次数: 0

摘要

前言:构建并比较多个机器学习模型,利用术前多参数磁共振成像(MRI)提取的放射学特征预测宫颈癌淋巴结(LN)转移。方法:本研究回顾性纳入407例宫颈癌患者,随机分为训练组(n=284)和验证组(n=123)。在每位患者的对比增强t1加权成像、t2加权成像和弥散加权成像上,从感兴趣的肿瘤区域中提取了4065个放射学特征。采用Mann-Whitney U检验、Spearman相关分析和选择算子Cox回归分析进行放射学特征选择。使用五种机器学习算法分析MRI放射学特征与LN状态之间的关系。通过测量接收机工作特性曲线下面积(AUC)和精度(ACC)来评价模型的性能。此外,Kaplan-Meier分析用于验证选定的临床和放射学特征的预后价值。结果:病理检查发现淋巴结转移者24.3%(99/407)。经过三步特征选择,采用18个放射学特征进行模型构建。与其他模型相比,XGBoost模型的AUC、准确度、灵敏度、特异度和F1评分分别为0.9268、0.8969、0.7419、0.9891和0.8364。此外,Kaplan-Meier曲线显示宫颈癌患者放射组学评分与无进展生存期有显著相关性(p < 0.05)。讨论:在机器学习模型中,XGBoost对淋巴结转移的预测能力最好,并通过放射学评分显示预后价值,突出了其临床潜力。结论:基于机器学习的多参数MRI放射学分析在宫颈癌淋巴结转移的术前预测和临床预后方面具有良好的应用前景。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Machine Learning based Radiomics from Multi-parametric Magnetic Resonance Imaging for Predicting Lymph Node Metastasis in Cervical Cancer.

Introduction: Construct and compare multiple machine learning models to predict lymph node (LN) metastasis in cervical cancer, utilizing radiomic features extracted from preoperative multi-parametric magnetic resonance imaging (MRI).

Methods: This study retrospectively enrolled 407 patients with cervical cancer who were randomly divided into a training cohort (n=284) and a validation cohort (n=123). A total of 4065 radiomic features were extracted from the tumor regions of interest on contrast-enhanced T1-weighted imaging, T2-weighted imaging, and diffusion-weighted imaging for each patient. The Mann-Whitney U test, Spearman correlation analysis, and selection operator Cox regression analysis were employed for radiomic feature selection. The relationship between MRI radiomic features and LN status was analyzed using five machine-learning algorithms. Model performance was evaluated by measuring the area under the receiver-operating characteristic curve (AUC) and accuracy (ACC). Moreover, Kaplan-Meier analysis was used to validate the prognostic value of selected clinical and radiomic characteristics.

Results: LN metastasis was pathologically detected in 24.3% (99/407) of patients. Following a three-step feature selection, 18 radiomic features were employed for model construction. The XGBoost model exhibited superior performance compared to other models, achieving an AUC, accuracy, sensitivity, specificity, and F1 score of 0.9268, 0.8969, 0.7419, 0.9891, and 0.8364, respectively, on the validation set. Additionally, Kaplan-Meier curves indicated a significant correlation between radiomic scores and progression-free survival in cervical cancer patients (p < 0.05).

Discussion: Among the machine learning models, XGBoost demonstrated the best predictive ability for LN metastasis and showed prognostic value through its radiomic score, highlighting its clinical potential.

Conclusion: Machine learning-based multi-parametric MRI radiomic analysis demonstrated promising performance in the preoperative prediction of LN metastasis and clinical prognosis in cervical cancer.

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来源期刊
CiteScore
2.60
自引率
0.00%
发文量
246
审稿时长
1 months
期刊介绍: Current Medical Imaging Reviews publishes frontier review articles, original research articles, drug clinical trial studies and guest edited thematic issues on all the latest advances on medical imaging dedicated to clinical research. All relevant areas are covered by the journal, including advances in the diagnosis, instrumentation and therapeutic applications related to all modern medical imaging techniques. The journal is essential reading for all clinicians and researchers involved in medical imaging and diagnosis.
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