Advances in PET imaging of cancer

In this Review, Schwenck et al. discuss how PET imaging of cancer has advanced through its combination with CT and MRI and the development of an array of imaging probes, and how these innovations now need to be fully integrated into the clinic to improve cancer diagnostics and guide therapy. Molecular imaging has experienced enormous advancements in the areas of imaging technology, imaging probe and contrast development, and data quality, as well as machine learning-based data analysis. Positron emission tomography (PET) and its combination with computed tomography (CT) or magnetic resonance imaging (MRI) as a multimodality PET-CT or PET-MRI system offer a wealth of molecular, functional and morphological data with a single patient scan. Despite the recent technical advances and the availability of dozens of disease-specific contrast and imaging probes, only a few parameters, such as tumour size or the mean tracer uptake, are used for the evaluation of images in clinical practice. Multiparametric in vivo imaging data not only are highly quantitative but also can provide invaluable information about pathophysiology, receptor expression, metabolism, or morphological and functional features of tumours, such as pH, oxygenation or tissue density, as well as pharmacodynamic properties of drugs, to measure drug response with a contrast agent. It can further quantitatively map and spatially resolve the intertumoural and intratumoural heterogeneity, providing insights into tumour vulnerabilities for target-specific therapeutic interventions. Failure to exploit and integrate the full potential of such powerful imaging data may lead to a lost opportunity in which patients do not receive the best possible care. With the desire to implement personalized medicine in the cancer clinic, the full comprehensive diagnostic power of multiplexed imaging should be utilized.

Automated summary

PET imaging of cancer has advanced through its combination with CT and MRI and the development of imaging probes. However, in clinical practice, only a few parameters are used for image evaluation. Multiparametric in vivo imaging data can provide quantitative and invaluable information about tumor biology, receptor expression, metabolism, morphology, and function, as well as drug pharmacodynamics, which should be fully utilized for personalized medicine in cancer care.