Maryam Fotouhi , Fardin Samadi Khoshe Mehr , Sina Delazar , Ramin Shahidi , Babak Setayeshpour , Mohssen Nassiri Toosi , Arvin Arian
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Visual features included tumor size, arterial-phase hyper-enhancement (APHE), washout, lesion segment, mass/mass-like, and capsule presence. Gini-importance method extracted the most important features to prevent over-fitting. Final dataset was split into training(70%), validation(10%), and test dataset(20%). The SVM model was used to train the classifying algorithm. For model validation, 5-fold cross-validation was utilized, and the test data set was used to assess the final accuracy. The area under the curve and receiver operating characteristic curves were used to assess the performance of the classifier model.</p></div><div><h3>Results</h3><p>For test dataset, the accuracy, sensitivity, and specificity values for classifying benign and HCC lesions were 82%,84%, and 81%, respectively. APHE, washout, tumor size, and mass/mass-like features significantly differentiated benign and HCC lesions with p-value < .001.</p></div><div><h3>Conclusions</h3><p>The developed classification model employing DCE-MRI features showed significant performance of visual features in classifying benign and HCC lesions. Our study also highlighted the significance of mass and mass-like features in addition to LI-RADS categorization. For future work, this study suggests developing a deep-learning algorithm for automatic lesion segmentation and feature assessment to reduce lesion categorization errors.</p></div>","PeriodicalId":38076,"journal":{"name":"European Journal of Radiology Open","volume":"11 ","pages":"Article 100535"},"PeriodicalIF":1.8000,"publicationDate":"2023-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2352047723000618/pdfft?md5=a99ac300c99ea0f71e8cae76a4bbfc49&pid=1-s2.0-S2352047723000618-main.pdf","citationCount":"0","resultStr":"{\"title\":\"Assessment of LI-RADS efficacy in classification of hepatocellular carcinoma and benign liver nodules using DCE-MRI features and machine learning\",\"authors\":\"Maryam Fotouhi , Fardin Samadi Khoshe Mehr , Sina Delazar , Ramin Shahidi , Babak Setayeshpour , Mohssen Nassiri Toosi , Arvin Arian\",\"doi\":\"10.1016/j.ejro.2023.100535\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><h3>Purpose</h3><p>The current study aimed to evaluate the efficiency of dynamic contrast-enhanced (DCE) MRI visual features in classifying benign liver nodules and hepatocellular carcinoma (HCC) using a machine learning model.</p></div><div><h3>Methods</h3><p>115 LI-RADS3, 137 LI-RADS4, and 140 LI-RADS5 nodules were included (392 nodules from 245 patients), which were evaluated by follow-up imaging for LR-3 and pathology results for LR-4 and LR-5 nodules. Data was collected retrospectively from 3 T and 1.5 T MRI scanners. All the lesions were categorized into 124 benign and 268 HCC lesions. Visual features included tumor size, arterial-phase hyper-enhancement (APHE), washout, lesion segment, mass/mass-like, and capsule presence. Gini-importance method extracted the most important features to prevent over-fitting. Final dataset was split into training(70%), validation(10%), and test dataset(20%). The SVM model was used to train the classifying algorithm. For model validation, 5-fold cross-validation was utilized, and the test data set was used to assess the final accuracy. The area under the curve and receiver operating characteristic curves were used to assess the performance of the classifier model.</p></div><div><h3>Results</h3><p>For test dataset, the accuracy, sensitivity, and specificity values for classifying benign and HCC lesions were 82%,84%, and 81%, respectively. APHE, washout, tumor size, and mass/mass-like features significantly differentiated benign and HCC lesions with p-value < .001.</p></div><div><h3>Conclusions</h3><p>The developed classification model employing DCE-MRI features showed significant performance of visual features in classifying benign and HCC lesions. Our study also highlighted the significance of mass and mass-like features in addition to LI-RADS categorization. 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引用次数: 0
摘要
目的本研究旨在利用机器学习模型评估动态对比增强(DCE) MRI视觉特征在良性肝结节和肝细胞癌(HCC)分类中的效率。方法纳入115例LI-RADS3、137例LI-RADS4和140例LI-RADS5结节(来自245例患者的392例结节),通过LR-3的随访影像学检查和LR-4、LR-5结节的病理结果进行评估。回顾性地收集了3t和1.5 T MRI扫描仪的数据。所有病变分为良性124例和HCC 268例。视觉特征包括肿瘤大小、动脉期超增强(APHE)、冲洗、病变节段、肿块/肿块样和包膜的存在。基尼重要度法提取最重要的特征,防止过拟合。最终数据集分为训练(70%)、验证(10%)和测试数据集(20%)。采用SVM模型对分类算法进行训练。模型验证采用5次交叉验证,并使用测试数据集评估最终准确性。使用曲线下面积和接收者工作特征曲线来评估分类器模型的性能。结果对于测试数据集,分类良性和HCC病变的准确性、敏感性和特异性分别为82%、84%和81%。APHE、冲洗、肿瘤大小、肿块/肿块样特征与良性和HCC病变有显著区别,p值<措施。结论建立的DCE-MRI特征分型模型对良性和HCC病变具有较好的视觉表现。除了LI-RADS分类外,我们的研究还强调了质量和类质量特征的重要性。对于未来的工作,本研究建议开发一种用于自动病灶分割和特征评估的深度学习算法,以减少病灶分类错误。
Assessment of LI-RADS efficacy in classification of hepatocellular carcinoma and benign liver nodules using DCE-MRI features and machine learning
Purpose
The current study aimed to evaluate the efficiency of dynamic contrast-enhanced (DCE) MRI visual features in classifying benign liver nodules and hepatocellular carcinoma (HCC) using a machine learning model.
Methods
115 LI-RADS3, 137 LI-RADS4, and 140 LI-RADS5 nodules were included (392 nodules from 245 patients), which were evaluated by follow-up imaging for LR-3 and pathology results for LR-4 and LR-5 nodules. Data was collected retrospectively from 3 T and 1.5 T MRI scanners. All the lesions were categorized into 124 benign and 268 HCC lesions. Visual features included tumor size, arterial-phase hyper-enhancement (APHE), washout, lesion segment, mass/mass-like, and capsule presence. Gini-importance method extracted the most important features to prevent over-fitting. Final dataset was split into training(70%), validation(10%), and test dataset(20%). The SVM model was used to train the classifying algorithm. For model validation, 5-fold cross-validation was utilized, and the test data set was used to assess the final accuracy. The area under the curve and receiver operating characteristic curves were used to assess the performance of the classifier model.
Results
For test dataset, the accuracy, sensitivity, and specificity values for classifying benign and HCC lesions were 82%,84%, and 81%, respectively. APHE, washout, tumor size, and mass/mass-like features significantly differentiated benign and HCC lesions with p-value < .001.
Conclusions
The developed classification model employing DCE-MRI features showed significant performance of visual features in classifying benign and HCC lesions. Our study also highlighted the significance of mass and mass-like features in addition to LI-RADS categorization. For future work, this study suggests developing a deep-learning algorithm for automatic lesion segmentation and feature assessment to reduce lesion categorization errors.