Columbia University Vagellos College of Physicians and Surgeons, Department of Radiology, New York, New York City, USA; Department of Radiology New York Presbyterian Hospital, USA.
Columbia University Vagellos College of Physicians and Surgeons, Department of Radiology, New York, New York City, USA; Department of Radiology New York Presbyterian Hospital, USA.
Eur J Radiol. 2020 Apr;125:108850. doi: 10.1016/j.ejrad.2020.108850. Epub 2020 Jan 28.
The clinical adoption of quantitative imaging biomarkers (radiomics) has established the need for high quality contrast-enhancement in medical images. We aimed to develop a machine-learning algorithm for Quality Control of Contrast-Enhancement on CT-scan (CECT-QC).
Multicenter data from four independent cohorts [A, B, C, D] of patients with measurable liver lesions were analyzed retrospectively (patients:time-points; 503:3397): [A] dynamic CTs from primary liver cancer (60:2359); [B] triphasic CTs from primary liver cancer (31:93); [C] triphasic CTs from hepatocellular carcinoma (121:363); [D] portal venous phase CTs of liver metastasis from colorectal cancer (291:582). Patients from cohort A were randomized to training-set (48:1884) and test-set (12:475). A random forest classifier was trained and tested to identify five contrast-enhancement phases. The input was the mean intensity of the abdominal aorta and the portal vein measured on a single abdominal CT scan image at a single time-point. The output to be predicted was: non-contrast [NCP], early-arterial [E-AP], optimal-arterial [O-AP], optimal-portal [O-PVP], and late-portal [L-PVP]. Clinical utility was assessed in cohorts B, C, and D.
The CECT-QC algorithm showed performances of 98 %, 90 %, and 84 % for predicting NCP, O-AP, and O-PVP, respectively. O-PVP was reached in half of patients and was associated with a peak in liver malignancy density. Contrast-enhancement quality significantly influenced radiomics features deciphering the phenotype of liver neoplasms.
A single CT-image can be used to differentiate five contrast-enhancement phases for radiomics-based precision medicine in the most common liver neoplasms occurring in patients with or without liver cirrhosis.
定量成像生物标志物（放射组学）的临床应用已经确立了对医学图像中高质量对比增强的需求。我们旨在开发一种用于 CT 扫描（CECT-QC）增强对比度质量控制的机器学习算法。
回顾性分析来自四个独立队列[A、B、C、D]的可测量肝病变患者的多中心数据（患者：时间点；503：3397）：[A]原发性肝癌的动态 CT（60：2359）；[B]原发性肝癌的三期 CT（31：93）；[C]肝细胞癌的三期 CT（121：363）；[D]结直肠癌肝转移的门静脉期 CT（291：582）。来自队列 A 的患者被随机分配到训练集（48：1884）和测试集（12：475）。使用随机森林分类器来识别五个对比增强阶段。输入是在单个腹部 CT 扫描图像上在单个时间点测量的腹主动脉和门静脉的平均强度。要预测的输出是：非对比[NCP]、早期动脉[E-AP]、最佳动脉[O-AP]、最佳门静脉[O-PVP]和晚期门静脉[L-PVP]。在队列 B、C 和 D 中评估了临床实用性。
CECT-QC 算法在预测 NCP、O-AP 和 O-PVP 时的性能分别为 98%、90%和 84%。在一半的患者中达到了 O-PVP，并且与肝恶性肿瘤密度的峰值相关。对比增强质量显著影响了用于解析肝肿瘤表型的放射组学特征。
单个 CT 图像可用于区分五种对比增强阶段，用于基于放射组学的最常见肝肿瘤的精准医学，这些肿瘤发生在有或没有肝硬化的患者中。
