Perinodular and Intranodular Radiomic Features on Lung CT Images Distinguish Adenocarcinomas from Granulomas
Perinodular and Intranodular Radiomic Features on Lung CT Images Distinguish Adenocarcinomas from Granulomas
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DOI：
10.1148/radiol.2018180910
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发表时间：
2019-03-01
期刊：
影响因子：
19.7
通讯作者：
Madabhushi, Anant
中科院分区：
文献类型：
--
作者：
Beig, Niha;Khorrami, Mohammadhadi;Madabhushi, Anant
目的：评价非小细胞肺癌非增强CT影像特征对非小细胞肺癌与肉芽肿的鉴别诊断能力。材料与方法：收集2007-2013年间两所医院290例非小细胞肺癌患者的CT影像资料，其中男性125例，平均67岁；女性165例，平均年龄68岁；年龄33-92岁。对每个患者一个结节进行组织病理学分析。相应的感兴趣结节在轴位CT图像上由放射科医生手动注解确定。从结节内和结节周围区域提取结节形状、小波(Gabor)和基于纹理(Haralick And Law Energy)的特征。为了训练145名患者的机器学习分类器，对特征进行了修剪。在145名患者的测试集中，将分类器的结果与卷积神经网络(CNN)和两名放射科医生的诊断读数进行了比较。结果：具有结节内放射特征的支持向量机分类器在测试集上的接收器操作特征曲线(AUC)下的面积为0.75。结合结节内和结周的放射组学，AUC提高到0.80。在同一测试集上，CNN的AUC为0.76。放射科读者获得的AUC值分别为0.61和0.60。结论：在非增强CT上，结节内和结节周围区域的放射学特征可以区分非小细胞肺癌腺癌和良性肉芽肿。(C)RSNA，2018年
Purpose: To evaluate ability of radiomic (computer-extracted imaging) features to distinguish non-small cell lung cancer adenocarcinomas from granulomas at noncontrast CT.Materials and Methods: For this retrospective study, screening or standard diagnostic noncontrast CT images were collected for 290 patients (mean age, 68 years; range, 18-92 years; 125 men [mean age, 67 years; range, 18-90 years] and 165 women [mean age, 68 years; range, 33-92 years]) from two institutions between 2007 and 2013. Histopathologic analysis was available for one nodule per patient. Corresponding nodule of interest was identified on axial CT images by a radiologist with manual annotation. Nodule shape, wavelet (Gabor), and texture-based (Haralick and Laws energy) features were extracted from intra- and perinodular regions. Features were pruned to train machine learning classifiers with 145 patients. In a test set of 145 patients, classifier results were compared against a convolutional neural network (CNN) and diagnostic readings of two radiologists.Results: Support vector machine classifier with intranodular radiomic features achieved an area under the receiver operating characteristic curve (AUC) of 0.75 on the test set. Combining radiomics of intranodular with perinodular regions improved the AUC to 0.80. On the same test set, CNN resulted in an AUC of 0.76. Radiologist readers achieved AUCs of 0.61 and 0.60, respectively.Conclusion: Radiomic features from intranodular and perinodular regions of nodules can distinguish non-small cell lung cancer adenocarcinomas from benign granulomas at noncontrast CT. (C) RSNA, 2018