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| 基于双序列MRI的计算机辅助诊断鉴别软组织肿瘤良恶性的研究 | |
| 引用本文： | 姜文研1,尚圣捷2,王颖妮2,王晓煜1,宋江典3,龙 哲2,于 韬1,罗娅红1. 基于双序列MRI的计算机辅助诊断鉴别软组织肿瘤良恶性的研究[J]. 现代肿瘤医学, 2020, 0(12): 2122-2126. DOI: 10.3969/j.issn.1672-4992.2020.12.028 |
| 作者姓名： | 姜文研1 尚圣捷2 王颖妮2 王晓煜1 宋江典3 龙 哲2 于 韬1 罗娅红1 |
| 作者单位： | 1.辽宁省肿瘤医院(中国医科大学肿瘤医院)，辽宁 沈阳 110042;2.中国医科大学生物医学工程系；3医学信息学院，辽宁 沈阳 110122 |
| 摘 要： | 目的:研究使用计算机辅助分析方法针对软组织肿瘤MRI影像进行肿瘤良恶性鉴别的价值。方法:回顾性收集了在辽宁省肿瘤医院就诊的72例软组织肿瘤患者的CE-T1和T1WI双序列MRI影像数据(2017年1月至2018年1月)。通过提取和筛选MRI影像特征,建立支持向量机(SVM)、K-最邻近(KNN)和随机森林(RF)三种机器学习分类器模型对肿瘤病灶进行二分类鉴别;提出一种新型集成学习分类器模型用于将两个序列MRI信息进行融合。通过绘制受试者工作特征曲线(ROC)并计算ROC曲线下面积(AUC值)以评估模型的分类鉴别能力。结果:三种机器学习分类器均取得较好的良恶性鉴别效果;提出的集成学习分类器的分类效果最佳,AUC值达到0.922(敏感性=0.965,特异性=0.783)。结论:本研究提出的计算机辅助模型能够利用MRI影像对软组织肿瘤良恶性进行有效的辅助鉴别,具有一定的潜在应用价值。 |
| 关 键 词： | 软组织肿瘤 MRI 计算机辅助分析 |
MRI-based computer-aided differentiation between benign and malignant soft-tissue tumors |
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| Jiang Wenyan1,Shang Shengjie2,Wang Yingni2,Wang Xiaoyu1,Song Jiangdian3,Long Zhe2,Yu Tao1,Luo Yahong1. MRI-based computer-aided differentiation between benign and malignant soft-tissue tumors[J]. Journal of Modern Oncology, 2020, 0(12): 2122-2126. DOI: 10.3969/j.issn.1672-4992.2020.12.028 | |
| Authors: | Jiang Wenyan1 Shang Shengjie2 Wang Yingni2 Wang Xiaoyu1 Song Jiangdian3 Long Zhe2 Yu Tao1 Luo Yahong1 |
| Affiliation: | 1.Cancer Hospital of China Medical University,Liaoning Cancer Hospital & Institute Liaoning,Liaoning Shenyang 110042,China；2.Department of Biomedical Engineering；3.College of Medical Informatics,China Medical University,Liaoning Shenyang 110122,China. |
| Abstract: | Objective:To analyze the identification of benign and malignant tumors using MRI images of soft tissue tumors by computer-aided diagnosis.Methods:CE-T1 and T1WI dual-sequence MRI image data were retrospectively collected from 72 soft-tissue tumors patients from Liaoning Provincial Cancer Hospital from Jan.2017 to Jan.2018.After extracting and selecting MRI image features,support vector machine(SVM),K-nearest neighbor（KNN) and random forest（RF) classifiers were established to discriminate tumor lesions.An ensemble learning classifier was proposed integrating both the CE-T1 and T1WI MRI.Operating characteristic curves（ROC) were plotted to assess the predictive abilities of the models with the area under the ROC curve（AUC).Results:The three machine learning classifiers all generated acceptable prediction results.The proposed ensemble learning classifier gave a highest AUC value of 0.922(sensitivity=0.965,specificity=0.783).Conclusion:The proposed ensemble learning classifier based on multimodal MRI data of soft-tissue tumors in this study has great potential in non-invasive distinguishing malignant from benign tumors. |
| Keywords: | soft-tissue tumors MRI CAD |
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