The purpose of this single-center retrospective study was to investigate the effectiveness of contrast-enhanced computed tomography (CECT)-based radiomic signatures for the preoperative prediction of pathological grades of hepatocellular carcinoma (HCC) via machine learning. The authors found that the radiomics signatures could non-invasively explore the underlying association between CECT images and pathological grades of HCC. Key points The radiomics signatures may non-invasively explore the underlying association between CECT images and pathological grades of HCC via machine learning. The radiomics signatures of CECT images may enhance the prediction performance of pathological grading of HCC, and further validation is required. The features extracted from arterial phase CECT images may be more reliable than venous phase CECT images for predicting pathological grades of HCC. Article: Preoperative prediction for pathological grade of hepatocellular carcinoma via machine learning–based radiomics Authors: Bing Mao, Lianzhong Zhang, Peigang Ning, Feng Ding, Fatian Wu, Gary Lu, Yayuan Geng & Jingdong Ma
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
