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Liver metastases beyond classical imaging findings
Roberto Garcia-Figueiras1, Sandra Baleato-González2, Joan C Vilanova3,4
1Department of Radiology, Hospital Clínico Universitario de Santiago de Compostela and University odf Santiago de Compostela (USC), Santiago de Compostela, Spain.
Abdominal Radiology (New York)
|August 10, 2026
Summary
Advanced imaging, radiomics, and AI are transforming liver metastases assessment. These tools enhance detection, characterization, and prediction of treatment response, moving beyond traditional methods for better patient outcomes.
Area of Science:
- Radiology
- Oncology
- Medical Imaging
Background:
- Liver metastases are the most common secondary liver cancer and significantly impact patient prognosis.
- Current imaging techniques are crucial for detecting, characterizing, and planning treatment for liver metastases.
- There's a growing need to assess biological characteristics for personalized treatment strategies.
Purpose of the Study:
- To provide a comprehensive overview of current and emerging imaging techniques for liver metastases.
- To highlight innovations in assessing biological heterogeneity and predicting treatment response.
- To discuss challenges and future directions in advanced liver metastases imaging.
Main Methods:
- Review of classical and advanced imaging modalities.
- Exploration of radiomics and artificial intelligence (AI) applications.
- Analysis of imaging's role in assessing histopathologic, molecular, and genetic features.
Main Results:
- Advanced imaging, radiomics, and AI enhance the assessment of tumor heterogeneity.
- These techniques improve predictions of therapeutic response and patient outcomes.
- Imaging can now provide insights into biological characteristics beyond morphology.
Conclusions:
- A paradigm shift is occurring from morphologic to biologically informed imaging for liver metastases.
- Advanced imaging holds significant potential for lesion characterization, prognostic stratification, and treatment planning.
- Overcoming implementation challenges is key to integrating these advanced techniques into clinical practice.

