Xiong Xing, Wang Jia, Hu Su, Dai Yao, Zhang Yu, Hu Chunhong
Department of Radiology, The First Affiliated Hospital of Soochow University, Suzhou, China.
Institute of Medical Imaging, Soochow University, Suzhou, China.
Front Oncol. 2021 Feb 24;11:601699. doi: 10.3389/fonc.2021.601699. eCollection 2021.
To determine whether machine learning based on conventional magnetic resonance imaging (MRI) sequences have the potential for the differential diagnosis of multiple myeloma (MM), and different tumor metastasis lesions of the lumbar vertebra.
We retrospectively enrolled 107 patients newly diagnosed with MM and different metastasis of the lumbar vertebra. In total 60 MM lesions and 118 metastasis lesions were selected for training classifiers (70%) and subsequent validation (30%). Following segmentation, 282 texture features were extracted from both T1WI and T2WI images. Following regression analysis using the least absolute shrinkage and selection operator (LASSO) algorithm, the following machine learning models were selected: Support-Vector Machine (SVM), K-Nearest Neighbor (KNN), Random Forest (RF), Artificial Neural Networks (ANN), and Naïve Bayes (NB) using 10-fold cross validation, and the performances were evaluated using a confusion matrix. Matthews correlation coefficient (MCC), sensitivity, specificity, and accuracy of the models were also calculated.
To differentiate MM and metastasis, 13 features in the T1WI images and 9 features in the T2WI images were obtained. Among the 10 classifiers, the ANN classifier from the T2WI images achieved the best performance (MCC = 0.605) with accuracy, sensitivity, and specificity of 0.815, 0.879, and 0.790, respectively, in the validation cohort. To differentiate MM and metastasis subtypes, eight features in the T1WI images and seven features in the T2WI images were obtained. Among the 10 classifiers, the ANN classifier from the T2WI images achieved the best performance (MCC = 0.560, 0.412, 0.449), respectively, with accuracy = 0.648; sensitivity 0.714, 0.821, 0.897 and specificity 0.775, 0.600, 0.640 for the MM, lung, and other metastases, respectively, in the validation cohort.
Machine learning-based classifiers showed a satisfactory performance in differentiating MM lesions from those of tumor metastasis. While their value for distinguishing myeloma from different metastasis subtypes was moderate.
确定基于传统磁共振成像（MRI）序列的机器学习是否具有鉴别多发性骨髓瘤（MM）以及腰椎不同肿瘤转移病灶的潜力。
我们回顾性纳入了107例新诊断为MM及腰椎不同转移情况的患者。总共选取60个MM病灶和118个转移病灶用于训练分类器（70%）及后续验证（30%）。分割后，从T1WI和T2WI图像中提取282个纹理特征。使用最小绝对收缩和选择算子（LASSO）算法进行回归分析后，选用以下机器学习模型：支持向量机（SVM）、K近邻（KNN）、随机森林（RF）、人工神经网络（ANN）和朴素贝叶斯（NB），采用10折交叉验证，并使用混淆矩阵评估性能。还计算了模型的马修斯相关系数（MCC）、敏感性、特异性和准确性。
为鉴别MM和转移灶，在T1WI图像中获得13个特征，在T2WI图像中获得9个特征。在10个分类器中，来自T2WI图像的ANN分类器表现最佳（MCC = 0.605），在验证队列中的准确性、敏感性和特异性分别为0.815、0.879和0.790。为鉴别MM和转移亚型，在T1WI图像中获得8个特征，在T2WI图像中获得7个特征。在10个分类器中，来自T2WI图像的ANN分类器表现最佳（MCC分别为0.560、0.412、0.449），在验证队列中，对于MM、肺转移和其他转移，准确性分别为0.648；敏感性分别为0.714、0.821、0.897，特异性分别为0.775、0.600、0.640。
基于机器学习的分类器在鉴别MM病灶与肿瘤转移病灶方面表现出令人满意的性能。而其在区分骨髓瘤与不同转移亚型方面的价值中等。
