Machine learning-based analysis of CT radiomics model for prediction of colorectal metachronous liver metastases

• Published in: Abdominal imaging Vol. 46; no. 1; pp. 249 - 256
• Main Authors: Taghavi, Marjaneh, Trebeschi, Stefano, Simões, Rita, Meek, David B., Beckers, Rianne C. J., Lambregts, Doenja M. J., Verhoef, Cornelis, Houwers, Janneke B., van der Heide, Uulke A., Beets-Tan, Regina G. H., Maas, Monique
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.01.2021; Springer Nature B.V
• Subjects: Bayesian analysis; Biomarkers; Colorectal cancer; Colorectal carcinoma; Computed tomography; Correlation coefficients; Feature extraction; Gastroenterology; Hepatology; Image segmentation; Imaging; Learning algorithms; Liver; Liver cancer; Machine learning; Mathematical models; Medicine; Medicine & Public Health; Metastases; Metastasis; Parenchyma; Patients; Prediction models; Predictions; Radiology; Radiomics; Stability analysis; Technical; Tomography; Neoplasm metastasis; X-ray computed; Liver neoplasms; Colorectal cancer; Machine learning

Purpose Early identification of patients at risk of developing colorectal liver metastases can help personalizing treatment and improve oncological outcome. The aim of this study was to investigate in patients with colorectal cancer (CRC) whether a machine learning-based radiomics model can predict...