Bi-parametric magnetic resonance imaging based radiomics for the identification of benign and malignant prostate lesions: cross-vendor validation
Bi-parametric magnetic resonance imaging based radiomics for the identification of benign and malignant prostate lesions: cross-vendor validation
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基于双参数磁共振成像的放射组学用于识别良性和恶性前列腺病变：跨供应商验证
DOI：
10.1007/s13246-021-01022-1
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发表时间：
2021-06-01
影响因子：
4.4
通讯作者：
Xia，Wei
中科院分区：
文献类型：
--
作者：
Ji，Xuefu;Zhang，Jiayi;Xia，Wei
本研究的目的是建立基于双参数磁共振成像（BP-MRI）的放射组学模型来区分前列腺良恶性病变，并跨厂商验证模型的泛化能力。采用不同厂商的两台扫描仪，获取459例临床疑似前列腺癌患者的活检前BP-MRI数据（T2WI和表观弥散系数）。前列腺活检是诊断前列腺良恶性病变的参考标准。训练集为来自Siemens （Vendor 1）的168例患者数据，内测集为来自同一供应商的70例患者数据。外部测试集是来自GE （Vendor 2）的221例患者数据。病变感兴趣区域（ROI）由经验丰富的放射科医生手动划定。分别从T2WI和ADC的ROI中提取了851个放射组学特征，包括形状、一阶统计、纹理和小波特征。研究了两种特征排序方法（最小冗余最大相关性[MRMR]和Wilcoxon秩和检验[WRST]）和三种分类器（随机森林[RF]、支持向量机[SVM]和最小绝对收缩和选择算子[LASSO]回归）在构建单参数放射组学特征方面的有效性。结合最优单参数放射组学特征，构建双参数放射组学模型。采用多变量logistic回归，将双参数放射组学模型与年龄、前列腺特异抗原（PSA）值相结合，构建综合诊断模型。所有模型均在训练集中建立，并在内外部测试集中独立验证，通过受试者工作特征曲线下面积（Area Under the Receiver Operating Characteristic Curve， AUC）量化模型在前列腺良恶性病变诊断中的表现。计算每个模型内部和外部测试集的平均auc。采用非劣效性检验检验外试模型的AUC是否不劣于内试模型的AUC。结合MRMR和LASSO产生了最佳的单参数放射组学特征，T2WI的平均AUC最高为0.673（内测AUC = 0.729，外测AUC = 0.616,p= 0.569）， ADC的平均AUC最高为0.810（内测AUC = 0.822，外测AUC = 0.797,p= 0.102）。双参数放射组学模型的平均AUC为0.833（内部测试AUC = 0.867，外部测试AUC = 0.798,p= 0.051）。综合诊断模型的平均AUC提高到0.911（内测AUC = 0.935 vs外测AUC = 0.886,p= 0.010）。本文建立的前列腺良恶性病变综合诊断模型准确、通用性强。
The purpose of this study was to develop Bi-parametric Magnetic Resonance Imaging (BP-MRI) based radiomics models for differentiation between benign and malignant prostate lesions, and to cross-vendor validate the generalization ability of the models. The prebiopsy BP-MRI data (T2-Weighted Image [T2WI] and the Apparent Diffusion Coefficient [ADC]) of 459 patients with clinical suspicion of prostate cancer were acquired using two scanners from different vendors. The prostate biopsies are the reference standard for diagnosing benign and malignant prostate lesions. The training set was 168 patients’ data from Siemens (Vendor 1), and the inner test set was 70 patients’ data from the same vendor. The external test set was 221 patients’ data from GE (Vendor 2). The lesion Region of Interest (ROI) was manually delineated by experienced radiologists. A total of 851 radiomics features including shape, first-order statistical, texture, and wavelet features were extracted from ROI in T2WI and ADC, respectively. Two feature-ranking methods (Minimum Redundancy Maximum Relevance [MRMR] and Wilcoxon Rank-Sum Test [WRST]) and three classifiers (Random Forest [RF], Support Vector Machine [SVM], and the Least Absolute Shrinkage and Selection Operator [LASSO] regression) were investigated for their efficacy in building single-parametric radiomics signatures. A biparametric radiomics model was built by combining the optimal single-parametric radiomics signatures. A comprehensive diagnosis model was built by combining the biparametric radiomics model with age and Prostate Specific Antigen (PSA) value using multivariable logistic regression. All models were built in the training set and independently validated in the inner and external test sets, and the performances of models in the diagnosis of benign and malignant prostate lesions were quantified by the Area Under the Receiver Operating Characteristic Curve (AUC). The mean AUCs of the inner and external test sets were calculated for each model. The non-inferiority test was used to test if the AUC of model in external test was not inferior to the AUC of model in inner test. Combining MRMR and LASSO produced the best-performing single-parametric radiomics signatures with the highest mean AUC of 0.673 for T2WI (inner test AUC = 0.729 vs. external test AUC = 0.616,p= 0.569) and the highest mean AUC of 0.810 for ADC (inner test AUC = 0.822 vs. external test AUC = 0.797,p= 0.102). The biparametric radiomics model produced a mean AUC of 0.833 (inner test AUC = 0.867 vs. external test AUC = 0.798,p= 0.051). The comprehensive diagnosis model had an improved mean AUC of 0.911 (inner test AUC = 0.935 vs. external test AUC = 0.886,p= 0.010). The comprehensive diagnosis model for differentiating benign from malignant prostate lesions was accurate and generalizable.