Sluijter Tim E, Roest Christian, Yakar Derya, Kwee Thomas C
Medical Imaging Center, Department of Radiology, University of Groningen, University Medical Center Groningen, 9700 RB Groningen, The Netherlands.
Diseases. 2025 Aug 1;13(8):242. doi: 10.3390/diseases13080242.
Computed tomography urography (CTU) is routinely used to evaluate the upper urinary tract in patients with hematuria. CTU may detect incidental findings outside the urinary tract, but it remains unclear if this adds value. This study aimed to develop a deep learning algorithm that automatically segments and selectively visualizes the urinary tract on CTU. : The urinary tract (kidneys, ureters, and urinary bladder) was manually segmented on 2 mm dual-phase CTU slices of 111 subjects. With this dataset, a deep learning-based AI was trained to automatically segment and selectively visualize the urinary tract on CTU scans (including accompanying unenhanced CT scans), which we dub "focused view CTU". Focused view CTU was technically optimized and tested in 39 subjects with hematuria. : The technically optimized focused view CTU algorithm provided complete visualization of 97.4% of kidneys, 80.8% of ureters, and 94.9% of urinary bladders. All urinary tract organs were completely visualized in 66.6% of cases. In these cases (excluding 33.3% of cases with incomplete visualization), focused view CTU intrinsically achieved a sensitivity, specificity, positive predictive value, and negative predictive value of 100.0%, 92.3%, 92.9%, and 100.0% for lesions in the urinary tract compared to unmodified CT, although interrater agreement was moderate (κ = 0.528). All incidental findings were successfully hidden by focused view CTU. : Focused view CTU provides adequate urinary tract segmentation in most cases, but further research is needed to optimize the technique (segmentation does not succeed in about one-third of cases). It offers selective urinary tract visualization, potentially aiding in assessing relevance and cost-effectiveness of detecting incidental findings in hematuria patients through a prospective randomized trial.
计算机断层扫描尿路造影（CTU）通常用于评估血尿患者的上尿路。CTU可能会检测到尿路外的偶然发现，但这种做法是否增加价值仍不清楚。本研究旨在开发一种深度学习算法，该算法能在CTU上自动分割并选择性地显示尿路。：在111名受试者的2毫米双期CTU切片上手动分割尿路（肾脏、输尿管和膀胱）。利用该数据集，训练了一种基于深度学习的人工智能，以在CTU扫描（包括附带的平扫CT扫描）上自动分割并选择性地显示尿路，我们将其称为“聚焦视图CTU”。聚焦视图CTU在技术上进行了优化，并在39名血尿患者中进行了测试。：技术优化后的聚焦视图CTU算法能完整显示97.4%的肾脏、80.8%的输尿管和94.9%的膀胱。66.6%的病例中所有尿路器官均能完整显示。在这些病例中（不包括33.3%显示不完整的病例），与未修改的CT相比，聚焦视图CTU对尿路病变的敏感性、特异性、阳性预测值和阴性预测值分别为100.0%、92.3%、92.9%和100.0%，尽管观察者间一致性为中等（κ = 0.528）。所有偶然发现均被聚焦视图CTU成功隐藏。：聚焦视图CTU在大多数情况下能提供足够的尿路分割，但需要进一步研究来优化该技术（约三分之一的病例分割不成功）。它提供了选择性尿路可视化，可能有助于通过前瞻性随机试验评估血尿患者中检测偶然发现的相关性和成本效益。
