The efficacy of using a multiparametric magnetic resonance imaging-based radiomics model to distinguish glioma recurrence from pseudoprogression

IF 2.1 4区 医学 Q2 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING
Fang-Xiong Fu , Qin-Lei Cai , Guo Li , Xiao-Jing Wu , Lan Hong , Wang-Sheng Chen
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引用次数: 0

Abstract

Objective

The early differential diagnosis of the postoperative recurrence or pseudoprogression (psPD) of a glioma is of great guiding significance for individualized clinical treatment. This study aimed to evaluate the ability of a multiparametric magnetic resonance imaging (MRI)-based radiomics model to distinguish between the postoperative recurrence and psPD of a glioma early on and in a noninvasive manner.

Methods

A total of 52 patients with gliomas who attended the Hainan Provincial People's Hospital between 2000 and 2021 and met the inclusion criteria were selected for this study. 1137 and 1137 radiomic features were extracted from T1 enhanced and T2WI/FLAIR sequence images, respectively.After clearing some invalid information and LASSO screening, a total of 9 and 10 characteristic radiological features were extracted and randomly divided into the training set and the test set according to 7:3 ratio. Select-Kbest and minimum Absolute contraction and selection operator (LASSO) were used for feature selection. Support vector machine and logistic regression were used to form a multi-parameter model for training and prediction. The optimal sequence and classifier were selected according to the area under the curve (AUC) and accuracy.

Results

Radiomic models 1, 2 and 3 based on T1WI, T2FLAIR and T1WI + T2T2FLAIR sequences have better performance in the identification of postoperative recurrence and false progression of T1 glioma. The performance of model 2 is more stable, and the performance of support vector machine classifier is more stable. The multiparameter model based on CE-T1 + T2WI/FLAIR sequence showed the best performance (AUC:0.96, sensitivity: 0.87, specificity: 0.94, accuracy: 0.89,95% CI:0.93–1).

Conclusion

The use of multiparametric MRI-based radiomics provides a noninvasive, stable, and accurate method for differentiating between the postoperative recurrence and psPD of a glioma, which allows for timely individualized clinical treatment.

使用基于多参数磁共振成像的放射组学模型区分胶质瘤复发和假性进展的功效。
目的:胶质瘤术后复发或假性进展(psPD)的早期鉴别诊断对临床个体化治疗具有重要指导意义。本研究旨在评估基于多参数磁共振成像(MRI)的放射组学模型以非侵入性方式早期鉴别胶质瘤术后复发和假性进展的能力:本研究选择了 2000 年至 2021 年期间在海南省人民医院就诊并符合纳入标准的 52 例胶质瘤患者。在清除部分无效信息并进行LASSO筛选后,分别提取出9个和10个特征性放射学特征,并按照7:3的比例随机分为训练集和测试集。特征选择采用选择-Kbest 和最小绝对收缩与选择算子(LASSO)。使用支持向量机和逻辑回归形成多参数模型,用于训练和预测。根据曲线下面积(AUC)和准确率选择最佳序列和分类器:结果:基于 T1WI、T2FLAIR 和 T1WI + T2T2FLAIR 序列的放射组学模型 1、2 和 3 在识别 T1 胶质瘤术后复发和假性进展方面表现较好。模型 2 的性能更稳定,支持向量机分类器的性能更稳定。基于 CE-T1 + T2WI/FLAIR 序列的多参数模型表现最佳(AUC:0.96,灵敏度:0.87,特异度:0.94,准确度:0.89,95% CI:0.93-1):结论:基于多参数磁共振成像的放射组学技术为区分胶质瘤术后复发和psPD提供了一种无创、稳定、准确的方法,有助于及时进行个体化临床治疗。
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
Magnetic resonance imaging
Magnetic resonance imaging 医学-核医学
CiteScore
4.70
自引率
4.00%
发文量
194
审稿时长
83 days
期刊介绍: Magnetic Resonance Imaging (MRI) is the first international multidisciplinary journal encompassing physical, life, and clinical science investigations as they relate to the development and use of magnetic resonance imaging. MRI is dedicated to both basic research, technological innovation and applications, providing a single forum for communication among radiologists, physicists, chemists, biochemists, biologists, engineers, internists, pathologists, physiologists, computer scientists, and mathematicians.
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