CT, MRI, and radiomics studies of liver metastasis histopathological growth patterns: an up-to-date review

Tungsten-based nanoparticles as contrast agents for liver tumor detection using dual-energy computed tomography

“…However, conven-tional CT imaging techniques struggle to distinguish soft tissues without the use of the X-ray attenuating contrast media, which can present challenges when detecting liver tumors from surrounding normal tissues. [12][13][14][15] Dual-energy CT (DECT) is a type of spectral CT that has garnered considerable attention in recent years. By rapidly switching between high and low energy spectra, DECT generates monochromatic energy images, material decomposition images, energy spectrum curves, and effective atomic number maps, thereby overcoming beam-hardening artifacts.…”

Editorial for “Magnetic Resonance Deep Learning Radiomic Model Based on Distinct Metastatic Vascular Patterns for Evaluating Recurrence‐Free Survival in Hepatocellular Carcinoma”

“…Predicting RFS in HCC requires long-term monitoring and dynamic assessment of treatment response. 8 Most current prediction models only focus on baseline assessment and cannot adapt to changes in treatment course or disease progression. Developing dynamic predictive models that continuously incorporate new factors and adjust predictions over time is critical for the effective personalized management of HCC patients.…”

“…Predicting RFS in HCC requires long‐term monitoring and dynamic assessment of treatment response 8 . Most current prediction models only focus on baseline assessment and cannot adapt to changes in treatment course or disease progression.…”