Using a single abdominal computed tomography image to differentiate five contrast-enhancement phases: A machine-learning algorithm for radiomics-based precision medicine. Issue 125 (April 2020)

• Record Type: Journal Article
• Authors: Dercle, Laurent; Ma, Jingchen; Xie, Chuanmiao; Chen, Ai-ping; Wang, Deling; Luk, Lyndon; Revel-Mouroz, Paul; Otal, Philippe; Peron, Jean-Marie; Rousseau, Hervé; Lu, Lin; Schwartz, Lawrence H.; Mokrane, Fatima-Zohra; Zhao, Binsheng
• Is Part Of: European journal of radiology. Issue 125(2020)
• Publication Date: 2020-04
• Subjects: AP arterial phase -- CE contrast enhanced -- CT Computed Tomography scan -- HCC Hepatocellular carcinoma -- NCP non-contrast phase -- PVP portal venous phase -- QC quality control; Machine learning -- Radiomics -- Quality control -- Contrast media -- Liver neoplasms; Medical radiology -- Periodicals; Radiology -- Periodicals; Radiologie médicale -- Périodiques; Medical radiology; Periodicals; 616.075705
• DOI: 10.1016/j.ejrad.2020.108850
• Languages: English
• ISSNs: 0720-048X

Abstract

Graphical abstract: Highlights: A single abdominal CT image can differentiate five contrast-enhancement phases. Performance was validated over a decade in multiple institutions across vendors. The tool was used for radiomics-based precision medicine in liver neoplasms. The portal venous phase was optimal in half of patients with liver neoplasm. Contrast-enhancement was suboptimal in cirrhotic patients. Abstract: Purpose: 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). Method: 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. Results: 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. Conclusions: 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.