{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Dercle L"],"funding":["Alain Rahmouni French Society of Radiology-CERF","Fondation Philanthropia","National Cancer Institute","NCI NIH HHS","Fondation Nuovo-Soldati"],"pagination":["108850"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC9345686"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["125"],"pubmed_abstract":["<h4>Purpose</h4>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).<h4>Method</h4>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.<h4>Results</h4>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.<h4>Conclusions</h4>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."],"journal":["European journal of radiology"],"pubmed_title":["Using a single abdominal computed tomography image to differentiate five contrast-enhancement phases: A machine-learning algorithm for radiomics-based precision medicine."],"pmcid":["PMC9345686"],"funding_grant_id":["U01 CA225431"],"pubmed_authors":["Schwartz LH","Dercle L","Zhao B","Wang D","Luk L","Rousseau H","Mokrane FZ","Peron JM","Revel-Mouroz P","Lu L","Ma J","Chen AP","Xie C","Otal P"],"additional_accession":[]},"is_claimable":false,"name":"Using a single abdominal computed tomography image to differentiate five contrast-enhancement phases: A machine-learning algorithm for radiomics-based precision medicine.","description":"<h4>Purpose</h4>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).<h4>Method</h4>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.<h4>Results</h4>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.<h4>Conclusions</h4>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.","dates":{"release":"2020-01-01T00:00:00Z","publication":"2020 Apr","modification":"2025-04-18T13:16:03.667Z","creation":"2025-02-19T05:06:16.347Z"},"accession":"S-EPMC9345686","cross_references":{"pubmed":["32070870"],"doi":["10.1016/j.ejrad.2020.108850"]}}