Jendeberg Johan, Thunberg Per, Lidén Mats
Department of Radiology, Faculty of Medicine and Health, Örebro University Hospital, 70185, Örebro, Sweden.
Department of Medical Physics, Faculty of Medicine and Health, Örebro University, Örebro, Sweden.
Urolithiasis. 2021 Feb;49(1):41-49. doi: 10.1007/s00240-020-01180-z. Epub 2020 Feb 27.
The objectives were to develop and validate a Convolutional Neural Network (CNN) using local features for differentiating distal ureteral stones from pelvic phleboliths, compare the CNN method with a semi-quantitative method and with radiologists' assessments and to evaluate whether the assessment of a calcification and its local surroundings is sufficient for discriminating ureteral stones from pelvic phleboliths in non-contrast-enhanced CT (NECT). We retrospectively included 341 consecutive patients with acute renal colic and a ureteral stone on NECT showing either a distal ureteral stone, a phlebolith or both. A 2.5-dimensional CNN (2.5D-CNN) model was used, where perpendicular axial, coronal and sagittal images through each calcification were used as input data for the CNN. The CNN was trained on 384 calcifications, and evaluated on an unseen dataset of 50 stones and 50 phleboliths. The CNN was compared to the assessment by seven radiologists who reviewed a local 5 × 5 × 5 cm image stack surrounding each calcification, and to a semi-quantitative method using cut-off values based on the attenuation and volume of the calcifications. The CNN differentiated stones and phleboliths with a sensitivity, specificity and accuracy of 94%, 90% and 92% and an AUC of 0.95. This was similar to a majority vote accuracy of 93% and significantly higher (p = 0.03) than the mean radiologist accuracy of 86%. The semi-quantitative method accuracy was 49%. In conclusion, the CNN differentiated ureteral stones from phleboliths with higher accuracy than the mean of seven radiologists' assessments using local features. However, more than local features are needed to reach optimal discrimination.
目的是开发并验证一种利用局部特征的卷积神经网络（CNN），用于区分远端输尿管结石和盆腔静脉石，将CNN方法与半定量方法以及放射科医生的评估进行比较，并评估在非增强CT（NECT）中对钙化及其局部周围环境的评估是否足以区分输尿管结石和盆腔静脉石。我们回顾性纳入了341例连续的急性肾绞痛患者，其NECT上有输尿管结石，表现为远端输尿管结石、静脉石或两者皆有。使用了一个2.5维CNN（2.5D-CNN）模型，其中通过每个钙化的垂直轴向、冠状和矢状图像用作CNN的输入数据。CNN在384个钙化上进行训练，并在一个包含50个结石和50个静脉石的未见过的数据集上进行评估。将CNN与七位放射科医生的评估进行比较（他们查看了围绕每个钙化的局部5×5×5 cm图像堆栈），并与一种基于钙化衰减和体积的截断值的半定量方法进行比较。CNN区分结石和静脉石的敏感性、特异性和准确性分别为94%、90%和92%，AUC为0.95。这与多数投票准确率93%相似且显著高于放射科医生的平均准确率86%（p = 0.03）。半定量方法准确率为49%。总之，与使用局部特征的七位放射科医生评估平均值相比，CNN区分输尿管结石和静脉石的准确率更高。然而，要达到最佳区分需要的不仅仅是局部特征。
