Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
Department of Endocrine Neoplasia and Hormonal Disorders, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
Clin Radiol. 2019 Oct;74(10):818.e1-818.e7. doi: 10.1016/j.crad.2019.06.021. Epub 2019 Jul 27.
To compare the efficacy of computed tomography (CT) texture analysis and conventional evaluation by radiologists for differentiation between large adrenal adenomas and carcinomas.
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.
The textural predictive model achieved a mean accuracy of 82%, whereas the mean accuracy for the radiologists was 68.5% (p<0.0001). The interobserver agreements between the predictive model and radiologists 1 and 2 were 0.44 (p<0.0005; 95% confidence interval [CI]: 0.25-0.62) and 0.47 (p<0.0005; 95% CI: 0.28-0.66), respectively. The Dice similarity coefficient between the readers' image labels was 0.875±0.04.
CT texture analysis of large adrenal adenomas and carcinomas is likely to improve CT evaluation of adrenal cortical tumours.
比较计算机断层扫描（CT）纹理分析与放射科医生常规评估在鉴别大肾上腺腺瘤和癌之间的疗效。
对 2002 年 1 月至 2014 年 4 月期间安德森癌症中心的 54 名患者的 54 个经组织病理学证实的肾上腺肿块（平均大小=5.9cm；范围=4.1-10cm）进行定量 CT 纹理分析。该患者组包括 32 名女性（肿块评估时的平均年龄=59 岁）和 22 名男性（肿块评估时的平均年龄=61 岁）。在增强前和静脉期 CT 图像上对肾上腺病变进行标记，由三位不同的读者进行标记，并使用这些标签生成基于强度和基于几何的纹理特征。纹理特征和衰减值被视为基于随机森林的分类器的输入值。同样，两名放射科医生根据形态学标准对肾上腺病变进行分类。比较了预测准确性和观察者间的一致性。
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<0.0001）。预测模型与放射科医生 1 和 2 之间的观察者间一致性分别为 0.44（p<0.0005；95%置信区间[CI]：0.25-0.62）和 0.47（p<0.0005；95% CI：0.28-0.66）。读者图像标签之间的 Dice 相似性系数为 0.875±0.04。
大肾上腺腺瘤和癌的 CT 纹理分析可能会提高 CT 对肾上腺皮质肿瘤的评估。
