摘要
目的 探讨基于MRI T2WI、DWI、增强T1WI的多模态图像,利用深度学习自动识别腮腺肿瘤病理类型,通过与病理结果对照评判机器学习的效能.方法 选取经病理证实的 39 例腮腺肿瘤患者,其中多形性腺瘤 9 例,Warthin瘤13 例,恶性肿瘤 6 例,其他非肿瘤病变 11 例.MRI经标准化处理后通过 2D U-NET网络对肿瘤类型进行鉴别,对比单通道T2 图像、多通道T2、DWI和增强T1 图像输入对肿瘤亚型的分类效果.其中 29 例的 434 层图像为训练集,10 例的 156层图像为测试集.采用准确率、敏感度、特异度、F1 分数、准确度等指标评估病理分类效能.结果 采用T2 单通道输入训练时,对Warthin瘤的鉴别具有最高的F1 分数 59.2%、敏感度为 51.1%和准确度为 70.3%,多形性腺瘤次之.通过常规T2、DWI和增强T1 图像对Warthin瘤鉴别的敏感度、准确度、F1 分数分别为 79.5%、70.0%、74.5%,而判断多形性腺瘤的敏感度、特异度分别为 46.2%、81.5%.结论 通过腮腺肿瘤MRI,利用深度学习能有效识别Warthin瘤,多模态图像结合能提高Warthin瘤、多形性腺瘤识别的敏感度.
Abstract
Objective To automatically identify pathological types of parotid gland tumors using deep learning methods based on multiple parameters of MR T2,DWI and enhanced T1 images,and to evaluate the efficacy of deep learning by compar-ing with pathological results.Methods MRI images of 39 cases of parotid gland tumors confirmed by pathology,including 9 cases of pleomorphic adenoma,13 cases of Warthin tumor,6 cases of malignant tumor,and 11 cases of other non-tumor lesions,were selected in this work.After MR images were standardized,2D U-NET was used to identify tumor types,and the classifica-tion efficiency through the input of single-channel of T2 image,and multi-channel of T2,DWI and enhanced T1 image were com-pared respectively.The data of 29 cases were used for training sets and the data of 10 cases for test sets.Accuracy,sensitivity,specificity,F1 score and precision were calculated to evaluate the efficacy of tumor classification.Results When training with T2 single-channel input,Warthin tumor had the highest F1 score(59.2%),sensitivity(51.1%)and precision(70.3%),followed by pleomorphic adenoma.The sensitivity,accuracy and F1 scores of multi-channel input for Warthin tumor identification were 79.5%,70.0%and 74.5%,respectively,the sensitivity and specificity of polymorphic adenoma were 46.2%and 81.5%,respec-tively.Conclusion By analyzing the imaging characteristics of parotid gland tumors,the deep learning method can effectively identify Warthin tumor,and the combination of multi-parametric MR images can improve the sensitivity of Warthin tumor and pleomorphic adenoma recognition.
基金项目
新疆维吾尔自治区自然科学基金项目(2019D01C114)
NETL
NSTL
万方数据