Background Adrenal masses are common, but radiology reporting and recommendations for management can be variable. Purpose To create a machine learning algorithm to segment adrenal glands on contrast-enhanced CT images and classify glands as normal or mass-containing and to assess algorithm performance. Materials and Methods This retrospective study included two groups of contrast-enhanced abdominal CT examinations (development data set and secondary test set). Adrenal glands in the development data set were manually segmented by radiologists. Images in both the development data set and the secondary test set were manually classified as normal or mass-containing. Deep learning segmentation and classification models were trained on the development data set and evaluated on both data sets. Segmentation performance was evaluated with use of the Dice similarity coefficient (DSC), and classification performance with use of sensitivity and specificity. Results The development data set contained 274 CT examinations (251 patients; median age, 61 years; 133 women), and the secondary test set contained 991 CT examinations (991 patients; median age, 62 years; 578 women). The median model DSC on the development test set was 0.80 (IQR, 0.78-0.89) for normal glands and 0.84 (IQR, 0.79-0.90) for adrenal masses. On the development reader set, the median interreader DSC was 0.89 (IQR, 0.78-0.93) for normal glands and 0.89 (IQR, 0.85-0.97) for adrenal masses. Interreader DSC for radiologist manual segmentation did not differ from automated machine segmentation (P = .35). On the development test set, the model had a classification sensitivity of 83% (95% CI: 55, 95) and specificity of 89% (95% CI: 75, 96). On the secondary test set, the model had a classification sensitivity of 69% (95% CI: 58, 79) and specificity of 91% (95% CI: 90, 92). Conclusion A two-stage machine learning pipeline was able to segment the adrenal glands and differentiate normal adrenal glands from those containing masses. © RSNA, 2022 Online supplemental material is available for this article.

中文翻译：

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用于 CT 上正常和肾上腺肿块的肾上腺分割和分类的机器学习。


背景 肾上腺肿块很常见，但放射学报告和治疗建议可能各不相同。目的 创建一种机器学习算法，在对比增强 CT 图像上分割肾上腺，并将腺体分类为正常或含有肿块，并评估算法性能。材料和方法 这项回顾性研究包括两组增强腹部 CT 检查（开发数据集和二次测试集）。开发数据集中的肾上腺由放射科医生手动分割。开发数据集和辅助测试集中的图像均被手动分类为正常或含有质量。深度学习分割和分类模型在开发数据集上进行训练，并在两个数据集上进行评估。使用 Dice 相似系数 (DSC) 评估分割性能，并使用敏感性和特异性评估分类性能。结果 开发数据集包含 274 项 CT 检查（251 名患者；中位年龄 61 岁；133 名女性），辅助测试集包含 991 项 CT 检查（991 名患者；中位年龄 62 岁；578 名女性）。开发测试集上正常腺体的模型 DSC 中位数为 0.80（IQR，0.78-0.89），肾上腺肿块的中位数模型 DSC 为 0.84（IQR，0.79-0.90）。在开发读者组中，正常腺体的中位读者间 DSC 为 0.89（IQR，0.78-0.93），肾上腺肿块的中位读者间 DSC 为 0.89（IQR，0.85-0.97）。用于放射科医师手动分割的 Interreader DSC 与自动机器分割没有区别 (P = .35)。在开发测试集上，该模型的分类敏感性为 83% (95% CI: 55, 95)，特异性为 89% (95% CI: 75, 96)。 在辅助测试集上，模型的分类灵敏度为 69% (95% CI: 58, 79)，特异性为 91% (95% CI: 90, 92)。结论 两阶段机器学习流程能够分割肾上腺并将正常肾上腺与含有肿块的肾上腺区分开来。 © RSNA，2022 本文提供在线补充材料。