The authors of this study aimed to develop and validate a combined radiomics-clinical model to predict malignancy of cerebral compression fractures on CT. This study comprised 165 patients with vertebral compression fractures who were allocated to training and validation cohorts. The authors were able to determine that the combined radiomics-clinical model integrating clinical parameters with radiomics score could predict malignancy in vertebral compression fractures on CT with high discriminatory ability. Key points A combined radiomics-clinical model was constructed to predict malignancy of vertebral compression fractures on CT by combining clinical parameters and radiomics features. The model showed good calibration and discrimination in both training and validation cohorts. The model showed high accuracy in the stratification of patients into groups with low and high risk of malignant vertebral compression fractures. Article: Combined radiomics-clinical model to predict malignancy of vertebral compression fractures on CT Authors: Choong Guen Chee, Min A Yoon, Kyung Won Kim, Yusun Ko, Su Jung Ham, Young Chul Cho, Bumwoo Park & Hye Won Chung
Impact of deep learning reconstruction on radiation dose reduction and cancer risk in CT examinations
Deep‑learning reconstruction (DLR) shifts CT image formation from a hardware‑limited process to a data‑driven one. In our real‑world cohort of >10,000 body scans, we observed a
28. April 2025
