Department of Radiology, Massachusetts General Hospital, Harvard Medical School, 75 Blossom Court, Room 248, Boston, MA, 02114, USA.
Siemens Healthcare GmbH, Diagnostic Imaging Computed Tomography, Siemensstr. 3, 91301, Forchheim, Germany.
Int J Comput Assist Radiol Surg. 2020 Oct;15(10):1727-1736. doi: 10.1007/s11548-020-02212-0. Epub 2020 Jun 26.
Radiomics help move cross-sectional imaging into the domain of quantitative imaging to assess the lesions, their stoma as well as in their temporal monitoring. We applied and assessed the accuracy of radiomics for differentiating healthy liver from diffuse liver diseases (cirrhosis, steatosis, amiodarone deposition, and iron overload) on non-contrast abdomen CT images in an institutional-reviewed board-approved, retrospective study.
Our study included 300 adult patients (mean age 63 ± 16 years; 171 men, 129 women) who underwent non-contrast abdomen CT and had either a healthy liver (n = 100 patients) or an evidence of diffuse liver disease (n = 200). The diffuse liver diseases included steatosis (n = 50), cirrhosis (n = 50), hyperdense liver due to amiodarone deposition (n = 50), or iron overload (n = 50). We manually segmented the liver in one section at the level of the porta hepatis (all 300 patients) and then over the entire liver volume (50 patients). Radiomics were estimated for the liver, and statistical comparison was performed with multiple logistic regression and random forest classifier.
With random forest classifier, the AUC for radiomics ranged between 0.72 (iron overload vs. healthy liver) and 0.98 (hepatic steatosis vs. healthy liver) for differentiating diffuse liver disease from the healthy liver. Combined root mean square and gray-level co-occurrence matrix had the highest AUC (AUC:0.99, p < 0.01) for differentiating healthy liver from steatosis. Radiomics were more accurate for differentiating healthy liver from amiodarone (AUC:0.93) than from iron overload (AUC:0.79).
Radiomics enable differentiation of healthy liver from hepatic steatosis, cirrhosis, amiodarone deposition, and iron overload from a single section of non-contrast abdominal CT. The high accuracy of radiomics coupled with rapid segmentation of the region of interest, radiomics estimation, and statistical analyses within the same prototype makes a compelling case for bringing radiomics to clinical use for improving reporting in evaluation of healthy liver and diffuse liver diseases.
放射组学将横断面成像带入定量成像领域，以评估病变、其门脉以及在其时间监测中的情况。我们在一项机构审查委员会批准的回顾性研究中，应用并评估了放射组学在非对比腹部 CT 图像上区分健康肝脏与弥漫性肝脏疾病（肝硬化、脂肪变性、胺碘酮沉积和铁过载）的准确性。
我们的研究纳入了 300 名成年患者（平均年龄 63±16 岁；171 名男性，129 名女性），这些患者均行非对比腹部 CT 检查，且要么肝脏健康（100 例患者），要么存在弥漫性肝脏疾病的证据（200 例患者）。弥漫性肝脏疾病包括脂肪变性（50 例）、肝硬化（50 例）、由于胺碘酮沉积导致的高密度肝脏（50 例）或铁过载（50 例）。我们手动在肝门水平的一个层面上对肝脏进行分段（所有 300 例患者），然后对整个肝脏体积进行分段（50 例患者）。对肝脏进行放射组学估计，并使用多变量逻辑回归和随机森林分类器进行统计比较。
使用随机森林分类器，对于区分弥漫性肝脏疾病与健康肝脏，放射组学的 AUC 范围为 0.72（铁过载与健康肝脏）至 0.98（肝脂肪变性与健康肝脏）。综合均方根和灰度共生矩阵具有最高的 AUC（AUC：0.99，p<0.01），用于区分健康肝脏与脂肪变性。放射组学对于区分健康肝脏与胺碘酮（AUC：0.93）比区分健康肝脏与铁过载（AUC：0.79）更准确。
放射组学能够从非对比腹部 CT 的单个层面区分健康肝脏与肝脂肪变性、肝硬化、胺碘酮沉积和铁过载。放射组学的高精度与对感兴趣区域的快速分割、放射组学估计以及在同一原型内进行统计分析相结合，为将放射组学应用于临床，改善健康肝脏和弥漫性肝脏疾病评估报告提供了有力的依据。
