面向淋巴结病变多分类鉴别的弹性和B型双模态超声影像组学

Multi-class discrimination of lymphadenopathy by using dual-modal ultrasound radiomics with elastography and B-mode ultrasound

本文探讨弹性和B型超声双模态影像组学定量特征对淋巴结病变的多分类诊断意义。本文回顾性研究248例患者共251个淋巴结(良性89个,淋巴瘤70个,转移性92个)的弹性和B型双模态超声图像。首先提取弹性和B型超声的双模态影像组学定量特征,每个模态包括形态学特征、影像强度特征和灰度共生矩阵特征共212个特征;然后利用三种基于信息论的特征选择方法,即条件信息特征提取法、条件互信息最大化法和双输入对称相关性法,选取不同的影像组学特征子集;接着采用支持向量机在每个模态的影像组学特征子集上进行良性淋巴结、淋巴瘤和转移性淋巴结的多分类诊断;最后利用Adaboost算法融合不同模态和不同特征子集的分类结果。经过五折交叉验证的淋巴结病变多分类准确率和全组F1值分别达到76.09%±1.41%、75.88%±4.32%;选择良性淋巴结、淋巴瘤和转移性淋巴结分别为正样本时,多分类受试者操作特性曲线下面积分别为0.77、0.93和0.84。本文研究结果表明运用Adaboost融合双模态影像组学特征有助于提升淋巴结的多分类性能。本文方法有望用于三类淋巴结病变的辅助诊断。

The purpose of our study is to evaluate the diagnostic performance of radiomics in multi-class discrimination of lymphadenopathy based on elastography and B-mode dual-modal ultrasound images.We retrospectively analyzed a total of 251 lymph nodes(89 benign lymph nodes,70 lymphoma and 92 metastatic lymph nodes)from 248 patients,which were examined by both elastography and B-mode sonography.Firstly,radiomic features were extracted from multimodal ultrasound images,including shape features,intensity statistics features and gray-level cooccurrence matrix texture features.Secondly,three feature selection methods based on information theory were used on the radiomic features to select different subsets of radiomic features,consisting of conditional infomax feature extraction,conditional mutual information maximization,and double input symmetric relevance.Thirdly,the support vector machine classifier was performed for diagnosis of lymphadenopathy on each radiomic subsets.Finally,we fused the results from different modalities and different radiomic feature subsets with Adaboost to improve the performance of lymph node classification.The results showed that the accuracy and overall F1 score with five-fold cross-validation were76.09%±1.41%and 75.88%±4.32%,respectively.Moreover,when considering on benign lymph nodes,lymphoma or metastatic lymph nodes respectively,the area under the receiver operating characteristic curve of multi-class classification were 0.77,0.93 and 0.84,respectively.This study indicates that radiomic features derived from multimodal ultrasound images are benefit for diagnosis of lymphadenopathy.It is expected to be useful in clinical differentiation of lymph node diseases.