Purpose

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.

Methods

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.

Results

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).

Conclusion

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.

中文翻译：

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在非对比CT上鉴别放射性弥漫性肝病的放射学准确性。

目的

放射线学有助于将横截面成像转移到定量成像领域，以评估病变，造口以及时间监测。我们在经过机构审查的董事会批准的回顾性研究中，应用并评估了放射线学在区分非健康腹部CT图像上将健康肝脏与弥漫性肝病（肝硬化，脂肪变性，胺碘酮沉积和铁超负荷）区分开的准确性。

方法

我们的研究包括300例成人患者（平均年龄63±16岁； 171例男性，129例女性），他们接受了非对比腹部CT检查，并且肝脏健康（n  = 100例）或有弥漫性肝病的证据（n  = 200 ）。弥漫性肝脏疾病包括脂肪变性（n  = 50），肝硬化（n  = 50），由于胺碘酮沉积引起的高密度肝（n  = 50）或铁超负荷（n  = 50）。我们在肝门的水平上手动将肝脏分割成一个部分（所有300例患者），然后在整个肝脏中进行分割（50例患者）。估算了肝脏的放射学，并使用多元逻辑回归和随机森林分类器进行了统计比较。

结果

使用随机森林分类器，放射学的AUC范围在0.72（铁超载与健康肝脏）和0.98（肝脂肪变性与健康肝脏）之间，以区分弥散性肝病与健康肝脏。结合均方根和灰度共生矩阵具有最高的AUC（AUC：0.99，p  <0.01），以区分健康肝脏和脂肪变性。放射线学将健康的肝脏与胺碘酮（AUC：0.93）区分开来比铁超负荷（AUC：0.79）更准确。

结论

放射线学可通过无造影腹部CT的单个切片区分健康的肝脏与肝脂肪变性，肝硬化，胺碘酮沉积和铁超负荷。放射线学的高精度加上感兴趣区域的快速分割，放射线学估计和同一原型内的统计分析，为将放射线学投入临床使用以改善对健康肝脏和弥漫性肝病的评估报告提供了令人信服的案例。