Using a single abdominal computed tomography image to differentiate five contrast-enhancement phases: A machine-learning algorithm for radiomics-based precision medicine.
Using a single abdominal computed tomography image to differentiate five contrast-enhancement phases: A machine-learning algorithm for radiomics-based precision medicine.
复制标题
DOI：
10.1016/j.ejrad.2020.108850
复制
发表时间：
2020-04
影响因子：
3.3
通讯作者：
Zhao, Binsheng
中科院分区：
文献类型：
--
作者：
Dercle, Laurent;Ma, Jingchen;Xie, Chuanmiao;Chen, Ai-ping;Wang, Deling;Luk, Lyndon;Revel-Mouroz, Paul;Otal, Philippe;Peron, Jean-Marie;Rousseau, Herve;Lu, Lin;Schwartz, Lawrence H.;Mokrane, Fatima-Zohra;Zhao, Binsheng
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度增强的需求。我们的目的是开发一个机器学习算法的质量控制的对比度增强CT扫描（CECT-QC）。回顾性分析了来自4个独立队列[A、B、C、D]的可测量肝脏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]原发性肝癌的三相CT（31：93）; [C]肝细胞癌的三相CT（121：363）;[D]结直肠癌肝转移的门静脉期CT（291：582）。队列A的患者被随机分配至训练集（48：1884）和测试集（12：475）。随机森林分类器进行了训练和测试，以确定五个对比度增强阶段。输入是在单个时间点在单个腹部CT扫描图像上测量的腹主动脉和门静脉的平均强度。待预测的输出为：非造影剂[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中评估了临床效用。CECT-QC算法对NCP、O-AP和O-PVP的预测性能分别为98%、90%和84%。半数患者达到O-PVP，并与肝脏恶性肿瘤密度峰值相关。对比度增强质量显着影响放射组学特征解读肝肿瘤的表型。单个CT图像可用于区分五个对比度增强阶段，用于基于放射学的精确医学，用于患有或不患有肝硬化的患者中最常见的肝肿瘤。
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.
登录
查看更多内容
影响因子：
5.5
作者：
Pano Brufau, Blanca;Sebastia Cerqueda, Carmen;Nicolau Molina, Carlos
通讯作者：
Nicolau Molina, Carlos
影响因子：
45.3
作者：
Choi, Haesun;Charnsangavej, Chuslip;Benjamin, Robert S.
通讯作者：
Benjamin, Robert S.
影响因子：
158.5
作者：
Llovet, Josep M.;Ricci, Sergio;Bruix, Jordi
通讯作者：
Bruix, Jordi
影响因子：
13.5
作者：
Serste, Thomas;Barrau, Vincent;Degos, Francoise
通讯作者：
Degos, Francoise
影响因子：
168.9
作者：
Tsochatzis, Emmanuel A.;Bosch, Jaime;Burroughs, Andrew K.
通讯作者：
Burroughs, Andrew K.