Division of Radiology, Diagnostic Department, Geneva University Hospitals, Geneva, Switzerland.
Division of Urology, Department of Surgery, Geneva University Hospitals, Geneva, Switzerland.
Eur Radiol. 2019 Sep;29(9):4776-4782. doi: 10.1007/s00330-019-6004-7. Epub 2019 Feb 12.
Distinguishing between kidney stones and phleboliths can constitute a diagnostic challenge in patients undergoing unenhanced low-dose CT (LDCT) for acute flank pain. We sought to investigate the accuracy of radiomics and a machine-learning classifier in differentiating between kidney stones and phleboliths on LDCT.
Radiomics features were extracted following a semi-automatic segmentation of kidney stones and phleboliths for two independent consecutive cohorts of patients undergoing LDCT for acute flank pain. Radiomics features from the first cohort of patients (n = 369) were ultimately used to train a machine-learning model designed to distinguish kidney stones (n = 211) from phleboliths (n = 201). Classification performance was assessed on the second independent cohort (i.e., testing set) (kidney stones n = 24; phleboliths n = 23) using positive and negative predictive values (PPV and NPV), area under the receiver operating curves (AUC), and permutation testing.
Our machine-learning classification model trained on radiomics features achieved an overall accuracy of 85.1% on the independent testing set, with an AUC of 0.902, PPV of 81.5%, and NPV of 90.0%. Classification accuracy was significantly better than chance on permutation testing (p < 0.05, permutation p value).
Radiomics and machine learning enable accurate differentiation between kidney stones and phleboliths on LDCT in patients presenting with acute flank pain.
• Combining a machine-learning algorithm with radiomics features extracted for abdominopelvic calcification on LDCT offers a highly accurate method for discriminating phleboliths from kidney stones. • Our radiomics and machine-learning model proved robust for CT acquisition and reconstruction protocol when tested in comparison with an external independent cohort of patients with acute flank pain. • The high performance of the radiomics-based automatic classification model in differentiating phleboliths from kidney stones indicates its potential as a future diagnostic tool for equivocal abdominopelvic calcifications in the setting of suspected renal colic.
在因急性腰痛而行低剂量 CT（LDCT）检查的患者中，区分肾结石和静脉石可能具有诊断挑战性。我们旨在研究放射组学和机器学习分类器在 LDCT 上区分肾结石和静脉石的准确性。
对因急性腰痛而行 LDCT 的连续两批患者的肾结石和静脉石进行半自动分割后，提取放射组学特征。第一组患者（n=369）的放射组学特征最终用于训练机器学习模型，以区分肾结石（n=211）和静脉石（n=201）。使用阳性和阴性预测值（PPV 和 NPV）、受试者工作特征曲线下面积（AUC）和置换检验，在第二个独立队列（即测试集）（肾结石 n=24；静脉石 n=23）上评估分类性能。
我们基于放射组学特征训练的机器学习分类模型在独立测试集上的总体准确率为 85.1%，AUC 为 0.902，PPV 为 81.5%，NPV 为 90.0%。置换检验表明分类准确性明显优于随机猜测（p<0.05，置换 p 值）。
在因急性腰痛而行 LDCT 的患者中，放射组学和机器学习可准确区分肾结石和静脉石。
结合机器学习算法和 LDCT 腹部钙化的放射组学特征可提供一种高度准确的方法，用于区分静脉石和肾结石。
当与另一组因急性腰痛而行 CT 检查的外部独立患者队列进行比较时，我们的放射组学和机器学习模型在 CT 采集和重建方案方面表现出稳健性。
放射组学自动分类模型在区分静脉石和肾结石方面的高性能表明其可能成为疑似肾绞痛患者疑似腹部钙化的潜在诊断工具。
