Computer-Aided Diagnosis of Ground-Glass Opacity Nodules Using Open-Source Software for Quantifying Tumor Heterogeneity
Computer-Aided Diagnosis of Ground-Glass Opacity Nodules Using Open-Source Software for Quantifying Tumor Heterogeneity
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DOI：
10.2214/ajr.17.17857
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发表时间：
2017-12-01
影响因子：
5
通讯作者：
Jayender, Jagadeesan
中科院分区：
文献类型：
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作者：
Li, Ming;Narayan, Vivek;Jayender, Jagadeesan
OBJECTIVE.本研究的目的是开发从高分辨率CT获得的定量成像生物标志物，用于将磨玻璃样结节（GGN）分为不典型腺瘤样增生（AAH）、原位腺癌（AIS）、微创腺癌（MIA）和浸润腺癌（IAC）;评价对比增强在鉴别诊断中的作用;并开发和验证支持向量机（SVM）来预测GGN类型。使用定制软件定量248个GGN的异质性。采用单变量Kruskal-Wallis检验进行统计分析，以评价4个GGN组之间显著差异的指标。异质性度量用于训练SVM以学习和预测病变类型。在未增强和对比增强CT扫描中，57个异质性指标中的50个和51个在四个GGN组中分别显示出统计学显著差异。支持向量机预测病变类型的准确性比三个专家放射科医生。基于SVM算法将GGN分为四组的准确率为70.9%，而放射科医生的准确率为39.6%。支持向量机分类AIS和MIA结节的准确率为73.1%，放射科医师的准确率为35.7%。对于惰性与侵入性病变，SVM的准确性为88.1%，放射科医生的准确性为60.8%。我们发现对比增强并不能显著提高GGN的鉴别诊断。与由三位放射科医生进行的GGN分类相比，关于所有异质性度量训练的SVM在将病变分类为四组，区分AIS和MIA以及惰性和侵袭性病变方面显示出显著更高的准确性。对比增强并不能提高GGN的鉴别诊断。
OBJECTIVE. The purposes of this study are to develop quantitative imaging biomarkers obtained from high-resolution CTs for classifying ground-glass nodules (GGNs) into atypical adenomatous hyperplasia (AAH), adenocarcinoma in situ (AIS), minimally invasive adenocarcinoma (MIA), and invasive adenocarcinoma (IAC); to evaluate the utility of contrast enhancement for differential diagnosis; and to develop and validate a support vector machine (SVM) to predict the GGN type.MATERIALS AND METHODS. The heterogeneity of 248 GGNs was quantified using custom software. Statistical analysis with a univariate Kruskal-Wallis test was performed to evaluate metrics for significant differences among the four GGN groups. The heterogeneity metrics were used to train a SVM to learn and predict the lesion type.RESULTS. Fifty of 57 and 51 of 57 heterogeneity metrics showed statistically significant differences among the four GGN groups on unenhanced and contrast-enhanced CT scans, respectively. The SVM predicted lesion type with greater accuracy than did three expert radiologists. The accuracy of classifying the GGNs into the four groups on the basis of the SVM algorithm was 70.9%, whereas the accuracy of the radiologists was 39.6%. The accuracy of SVM in classifying the AIS and MIA nodules was 73.1%, and the accuracy of the radiologists was 35.7%. For indolent versus invasive lesions, the accuracy of the SVM was 88.1%, and the accuracy of the radiologists was 60.8%. We found that contrast enhancement does not significantly improve the differential diagnosis of GGNs.CONCLUSION. Compared with the GGN classification done by the three radiologists, the SVM trained regarding all the heterogeneity metrics showed significantly higher accuracy in classifying the lesions into the four groups, differentiating between AIS and MIA and between indolent and invasive lesions. Contrast enhancement did not improve the differential diagnosis of GGNs.