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Functional imaging in liver tumours.

Maxime Ronot¹, Ashley Kieran Clift², Valérie Vilgrain¹

¹Department of Radiology, APHP, University Hospitals Paris Nord Val de Seine, Beaujon, Clichy, Hauts-de-Seine, France; University Paris Diderot, Sorbonne Paris Cité, Paris, France; INSERM U1149, Centre de recherche biomédicale Bichat-Beaujon, CRB3, Paris, France.

Journal of Hepatology

|July 11, 2016

Summary

This summary is machine-generated.

Functional imaging provides crucial physiological data for liver cancer management. This review details techniques like MRI and molecular imaging for diagnosing and treating liver tumors.

Keywords:

Imaging Liver Metastases Tumours

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Area of Science:

Radiology
Oncology
Medical Imaging

Background:

Functional imaging assesses tissue physiology, complementing morphological imaging.
Liver tumors, including hepatocellular carcinoma and metastases, are significant clinical challenges.
Accurate tumor characterization is vital for effective treatment selection and follow-up.

Purpose of the Study:

To review major functional imaging modalities used in hepatic oncology.
To discuss their current applications in managing primary and secondary liver tumors.
To explore future advancements in the field.

Main Methods:

Review of functional imaging techniques such as diffusion-weighted MRI, perfusion imaging, and molecular imaging.
Analysis of their roles in lesion characterization, treatment selection, and follow-up for liver tumors.
Discussion of current clinical applications and future trends.

Main Results:

Functional imaging offers valuable physiological insights beyond anatomical details.
Specific modalities are pivotal for characterizing liver lesions based on tumor type and size.
These techniques aid in treatment planning and monitoring patient progress.

Conclusions:

Functional imaging plays a critical role in the management of liver cancer.
Its application spans diagnosis, treatment selection, and follow-up of hepatic malignancies.
Continued development promises enhanced capabilities in oncologic imaging.