This study assessed the predictive capacity of CT-enhanced radiomics models when determining microvascular invasion (MVI) for isolated hepatocellular carcinoma (HCC). The radiomics model was shown to be a promising noninvasive biomarker for preoperatively predicting MVI in individuals with a solitary HCC ≤ 5 cm and has applications in shaping personalized treatment policies. Key points: Radiomics features extracted at a 5-mm distance from the tumor could better predict hepatocellular carcinoma microvascular invasion. Peritumoral radiomics can be used to capture tumor heterogeneity and predict microvascular invasion. This radiomics model stands as a promising noninvasive biomarker for preoperatively predicting MVI in individuals. Article: Predicting microvascular invasion in small (≤ 5 cm) hepatocellular carcinomas using radiomics-based peritumoral analysis Authors: Fang Wang, Ming Cheng, Binbin Du, Jing Li, Liming Li, Wenpeng Huang & Jianbo Gao
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
