ArticleEuropean journal of radiology2020
Using a single abdominal computed tomography image to differentiate five contrast-enhancement phases: A machine-learning algorithm for radiomics-based precision medicine.
Article in European journal of radiology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 1 of them a synthesis that pooled it.
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Who cites it
14 citing papers in PubMed, 1 synthesis or guideline pooled it, 21 citations in OpenAlex.
- Effectiveness of Artificial Intelligence for Personalized Medicine in Neoplasms: A Systematic Review.BioMed research international · 2022Pooled it
- Trial
- Overcoming domain-specific challenges for artificial intelligence in abdominal oncology toward clinical translation.Discover oncology · 2026Review
- Radiomics-based diagnosis of carotid artery stenosis using non-contrast CT: model development and validation.European journal of medical research · 2025Article
- Enhancing risk stratification in diabetic gastric cancer: muscle-fat ratio from photon-counting CT as a predictor of postoperative complications.BMC medical imaging · 2025Article
- A two-step automatic identification of contrast phases for abdominal CT images based on residual networks.Insights into imaging · 2025Article
- Annotated test-retest dataset of lung cancer CT scan images reconstructed at multiple imaging parameters.Scientific data · 2024Article
- Machine learning-based identification of contrast-enhancement phase of computed tomography scans.PloS one · 2024Article
- Image-Guided Precision Medicine in the Diagnosis and Treatment of Pheochromocytomas and Paragangliomas.Cancers · 2023Review
- Contrast phase recognition in liver computer tomography using deep learning.Scientific reports · 2022Article
- Predicting death or recurrence of portal hypertension symptoms after TIPS procedures.European radiology · 2022Article
- Artificial intelligence in the detection, characterisation and prediction of hepatocellular carcinoma: a narrative review.Translational gastroenterology and hepatology · 2022Review
- Identifying Robust Radiomics Features for Lung Cancer by Using In-Vivo and Phantom Lung Lesions.Tomography (Ann Arbor, Mich.) · 2021Article
- Understanding Sources of Variation to Improve the Reproducibility of Radiomics.Frontiers in oncology · 2021Review
Corrections and comments
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Authors and funding
14 authors at 6 institutions in 3 countries.
Funding
Abstract
purposeThe 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).
methodMulticenter 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.
resultsThe 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.
conclusionsA 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.
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