基于阈值的结石分割和放射组学在通过单能量CT确定肾结石成分中的性能。

Performance of threshold-based stone segmentation and radiomics for determining the composition of kidney stones from single-energy CT.

作者信息

Kaviani Parisa, Primak Andrew, Bizzo Bernardo, Ebrahimian Shadi, Saini Sanjay, Dreyer Keith J, Kalra Mannudeep K

机构信息

Department of Radiology, Massachusetts General Hospital and Harvard Medical School, 75 Blossom Court, Suite 248, Boston, MA, 02114, USA.

Siemens Medical Solutions USA Inc, Malvern, PA, 19355, USA.

出版信息

Jpn J Radiol. 2023 Feb;41(2):194-200. doi: 10.1007/s11604-022-01349-z. Epub 2022 Nov 4.

DOI:10.1007/s11604-022-01349-z

PMID:36331701

Abstract

PURPOSE

Knowledge of kidney stone composition can help in patient management; urine composition analysis and dual-energy CT are frequently used to assess stone type. We assessed if threshold-based stone segmentation and radiomics can determine the composition of kidney stones from single-energy, non-contrast abdomen-pelvis CT.

METHODS

With IRB approval, we identified 218 consecutive patients (mean age 64 ± 13 years; male:female 138:80) with the presence of kidney stones on non-contrast, abdomen-pelvis CT and surgical or biochemical proof of their stone composition. CT examinations were performed on one of the seven multidetector-row scanners from four vendors (GE, Philips, Siemens, Toshiba). Deidentified CT images were processed with a radiomics prototype (Frontier, Siemens Healthineers) to segment the entire kidney volumes with an AI-based organ segmentation tool. We applied a threshold of 130 HU to isolate stones in the segmented kidneys and to estimate radiomics over the segmented stone volume. A coinvestigator verified kidney stone segmentation and adjusted the volume of interest to include the entire stone volume when necessary. We applied multiple logistic regression tests with precision recall plots to obtain area under the curve (AUC) using a built-in R statistical program.

RESULTS

The threshold-based stone segmentation successfully isolated kidney stones (uric acid: n = 102 patients, calcium oxalate/phosphate: n = 116 patients) in all patients. Radiomics differentiated between calcium and uric acid stones with an AUC of 0.78 (p < 0.01, 95% CI 0.73-0.83), 0.79 sensitivity, and 0.90 specificity regardless of CT vendors (GE CT: AUC = 0.82, p < 0.01, 95% CI 0.740-0896; Siemens CT: AUC = 0.77, 95% CI 0.700-0.846, p < 0.01).

CONCLUSION

Automated threshold-based stone segmentation and radiomics can differentiate between calcium oxalate/phosphate and urate stones from non-contrast, single-energy abdomen CT.

摘要

目的

了解肾结石成分有助于患者管理；尿液成分分析和双能CT常用于评估结石类型。我们评估了基于阈值的结石分割和影像组学是否能从单能量、非增强腹部-盆腔CT中确定肾结石的成分。

方法

经机构审查委员会批准，我们纳入了218例连续患者（平均年龄64±13岁；男∶女为138∶80），这些患者在非增强腹部-盆腔CT上有肾结石，且有结石成分的手术或生化证据。CT检查在来自四个供应商（GE、飞利浦、西门子、东芝）的七台多排探测器扫描仪之一上进行。对去识别化的CT图像使用影像组学原型软件（Frontier，西门子医疗）进行处理，通过基于人工智能的器官分割工具分割整个肾脏体积。我们应用130HU的阈值在分割后的肾脏中分离结石，并在分割后的结石体积上估计影像组学特征。一名共同研究者核实肾结石分割情况，并在必要时调整感兴趣体积以纳入整个结石体积。我们使用内置的R统计程序，应用多重逻辑回归测试和精确召回率图来获得曲线下面积（AUC）。

结果

基于阈值的结石分割在所有患者中成功分离出肾结石（尿酸结石：102例患者，草酸钙/磷酸盐结石：116例患者）。无论CT供应商如何，影像组学区分钙结石和尿酸结石的AUC为0.78（p<0.01，95%CI 0.73-0.83），灵敏度为0.79，特异度为0.90（GE CT：AUC=0.82，p<0.01，95%CI 0.740-0.896；西门子CT：AUC=0.77，95%CI 0.700-0.846，p<0.01）。