Machine learning-based texture analysis for differentiation of large adrenal cortical tumours on CT
Machine learning-based texture analysis for differentiation of large adrenal cortical tumours on CT
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DOI：
10.1016/j.crad.2019.06.021
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发表时间：
2019-10-01
影响因子：
2.6
通讯作者：
Elsayes, K. M.
中科院分区：
文献类型：
--
作者：
Elmohr, M. M.;Fuentes, D.;Elsayes, K. M.
目的：比较计算机断层扫描（CT）纹理分析与放射科医师常规评估对肾上腺大腺瘤与癌鉴别的疗效。材料与方法：采用定量CT结构分析对2002年1月至2014年4月安德森癌症中心收治的54例经组织病理学证实的肾上腺肿块（平均大小=5.9 cm，范围=4.1-10 cm）进行评估。患者组包括32名女性（肿块评估时平均年龄59岁）和22名男性（肿块评估时平均年龄61岁）。在对比前和静脉期CT图像上看到的肾上腺病变被三种不同的阅读器标记，并使用这些标记来生成基于强度和几何的纹理特征。将纹理特征和衰减值作为随机森林分类器的输入值。同样，肾上腺病变由两个不同的放射科医生根据形态学标准进行分类。比较了预测精度和观察者间一致性。结果：纹理预测模型的平均准确率为82%，而放射科医生的平均准确率为68.5% (p
AIM: To compare the efficacy of computed tomography (CT) texture analysis and conventional evaluation by radiologists for differentiation between large adrenal adenomas and carcinomas.MATERIALS AND METHODS: Quantitative CT texture analysis was used to evaluate 54 histopathologically proven adrenal masses (mean size=5.9 cm; range=4.1-10 cm) from 54 patients referred to Anderson Cancer Center from January 2002 through April 2014. The patient group included 32 women (mean age at mass evaluation=59 years) and 22 men (mean age at mass evaluation=61 years). Adrenal lesions seen on precontrast and venous-phase CT images were labelled by three different readers, and the labels were used to generate intensity- and geometry-based textural features. The textural features and the attenuation values were considered as input values for a random forest-based classifier. Similarly, the adrenal lesions were classified by two different radiologists based on morphological criteria. Prediction accuracy and interobserver agreement were compared.RESULTS: The textural predictive model achieved a mean accuracy of 82%, whereas the mean accuracy for the radiologists was 68.5% (p