Kline Timothy L, Korfiatis Panagiotis, Edwards Marie E, Warner Joshua D, Irazabal Maria V, King Bernard F, Torres Vicente E, Erickson Bradley J
Department of Radiology, Mayo Clinic College of Medicine, Rochester, MN, USA.
Division of Nephrology and Hypertension, Mayo Clinic, Rochester, MN, USA.
Nephrol Dial Transplant. 2016 Feb;31(2):241-8. doi: 10.1093/ndt/gfv314. Epub 2015 Aug 31.
Renal imaging examinations provide high-resolution information about the anatomic structure of the kidneys and are used to measure total kidney volume (TKV) in autosomal dominant polycystic kidney disease (ADPKD) patients. TKV has become the gold-standard image biomarker for ADPKD progression at early stages of the disease and is used in clinical trials to characterize treatment efficacy. Automated methods to segment the kidneys and measure TKV are desirable because of the long time requirement for manual approaches such as stereology or planimetry tracings. However, ADPKD kidney segmentation is complicated by a number of factors, including irregular kidney shapes and variable tissue signal at the kidney borders.
We describe an image processing approach that overcomes these problems by using a baseline segmentation initialization to provide automatic segmentation of follow-up scans obtained years apart. We validated our approach using 20 patients with complete baseline and follow-up T1-weighted magnetic resonance images. Both manual tracing and stereology were used to calculate TKV, with two observers performing manual tracings and one observer performing repeat tracings. Linear correlation and Bland-Altman analysis were performed to compare the different approaches.
Our automated approach measured TKV at a level of accuracy (mean difference ± standard error = 0.99 ± 0.79%) on par with both intraobserver (0.77 ± 0.46%) and interobserver variability (1.34 ± 0.70%) of manual tracings. All approaches had excellent agreement and compared favorably with ground-truth manual tracing with interobserver, stereological and automated approaches having 95% confidence intervals ∼ ± 100 mL.
Our method enables fast, cost-effective and reproducible quantification of ADPKD progression that will facilitate and lower the costs of clinical trials in ADPKD and other disorders requiring accurate, longitudinal kidney quantification. In addition, it will hasten the routine use of TKV as a prognostic biomarker in ADPKD.
肾脏成像检查可提供有关肾脏解剖结构的高分辨率信息,并用于测量常染色体显性多囊肾病(ADPKD)患者的总肾体积(TKV)。TKV已成为ADPKD疾病早期进展的金标准影像生物标志物,并用于临床试验以表征治疗效果。由于诸如体视学或平面测量法追踪等手动方法需要较长时间,因此需要自动分割肾脏并测量TKV的方法。然而,ADPKD肾脏分割因多种因素而变得复杂,包括肾脏形状不规则以及肾脏边界处组织信号的变化。
我们描述了一种图像处理方法,该方法通过使用基线分割初始化来克服这些问题,以提供对相隔数年获得的后续扫描的自动分割。我们使用20例具有完整基线和随访T1加权磁共振图像的患者验证了我们的方法。手动追踪和体视学均用于计算TKV,两名观察者进行手动追踪,一名观察者进行重复追踪。进行线性相关性和Bland-Altman分析以比较不同方法。
我们的自动方法测量TKV的准确度(平均差异±标准误差=0.99±0.79%)与手动追踪的观察者内(0.77±0.46%)和观察者间变异性(1.34±0.70%)相当。所有方法都具有极好的一致性,并且与真实手动追踪相比具有优势,观察者间、体视学和自动方法的95%置信区间约为±100 mL。
我们的方法能够快速、经济高效且可重复地量化ADPKD进展,这将有助于降低ADPKD和其他需要准确纵向肾脏量化的疾病的临床试验成本。此外,它将加速TKV作为ADPKD预后生物标志物的常规使用。