In the past few decades, tremendous evidence has emerged to drive the evolution of radiology from a qualitative discipline to a quantitative modern science. In particular, dynamic contrast-enhanced (DCE) imaging has provoked widespread research interest in oncological imaging because of its ability in the non-invasive characterization of microvascular information of tumors from imaging signals. The quantitative characteristics of the tissue microenvironment derived from this have significant value in addressing various clinical problems.
Remarkable progress has been made in the modeling of tracer kinetics that has greatly contributed to the development of advanced DCE theory, evolving from one compartment to two compartments over the years, from homogeneous compartment to distributed compartment, and from mixed transportation to separate account of blood flow and vessel wall permeability. Advanced DCE utilizes two kinetic parameters (BF and PS) to model the intravascular transportation and exchange between intravascular and extravascular space, reflecting blood flow and vessel wall permeability respectively. This is more advanced than the traditional DCE, which uses a single kinetic parameter (Ktrans) to describe these two types of transport. Besides the theoretical progress, imaging hardware has also made significant advances over the years, allowing the acquisition of DCE images with much-improved quality, which in turn enables the application of image processing algorithms such as registration, segmentation, etc to DCE imaging data and results in more reliable and more efficient performance. These advancements have spurred renewed interest in the application of DCE in clinical investigation, paving the way toward a more comprehensive assessment of tissue microcirculation in oncology.
This Research Topic aims to highlight the technological advancement in DCE and the clinical application of advanced DCE in oncology. We welcome original research, reviews, and methods articles focusing on but not limited to the following topics:
• Technical development in DCE, such as DCE image processing, AIF selection, and tracer kinetic modeling
• Application of advanced DCE to the differential diagnosis of tumor recurrence and treatment response
• Application of advanced DCE to tumor grading
• Application of advanced DCE to prognosis prediction
• Application of advanced DCE to genotype prediction.
Please note: manuscripts consisting solely of bioinformatics or computational analysis of public genomic or transcriptomic databases which are not accompanied by validation (independent cohort or biological validation in vitro or in vivo) are out of scope for this section and will not be accepted as part of this Research Topic.
In the past few decades, tremendous evidence has emerged to drive the evolution of radiology from a qualitative discipline to a quantitative modern science. In particular, dynamic contrast-enhanced (DCE) imaging has provoked widespread research interest in oncological imaging because of its ability in the non-invasive characterization of microvascular information of tumors from imaging signals. The quantitative characteristics of the tissue microenvironment derived from this have significant value in addressing various clinical problems.
Remarkable progress has been made in the modeling of tracer kinetics that has greatly contributed to the development of advanced DCE theory, evolving from one compartment to two compartments over the years, from homogeneous compartment to distributed compartment, and from mixed transportation to separate account of blood flow and vessel wall permeability. Advanced DCE utilizes two kinetic parameters (BF and PS) to model the intravascular transportation and exchange between intravascular and extravascular space, reflecting blood flow and vessel wall permeability respectively. This is more advanced than the traditional DCE, which uses a single kinetic parameter (Ktrans) to describe these two types of transport. Besides the theoretical progress, imaging hardware has also made significant advances over the years, allowing the acquisition of DCE images with much-improved quality, which in turn enables the application of image processing algorithms such as registration, segmentation, etc to DCE imaging data and results in more reliable and more efficient performance. These advancements have spurred renewed interest in the application of DCE in clinical investigation, paving the way toward a more comprehensive assessment of tissue microcirculation in oncology.
This Research Topic aims to highlight the technological advancement in DCE and the clinical application of advanced DCE in oncology. We welcome original research, reviews, and methods articles focusing on but not limited to the following topics:
• Technical development in DCE, such as DCE image processing, AIF selection, and tracer kinetic modeling
• Application of advanced DCE to the differential diagnosis of tumor recurrence and treatment response
• Application of advanced DCE to tumor grading
• Application of advanced DCE to prognosis prediction
• Application of advanced DCE to genotype prediction.
Please note: manuscripts consisting solely of bioinformatics or computational analysis of public genomic or transcriptomic databases which are not accompanied by validation (independent cohort or biological validation in vitro or in vivo) are out of scope for this section and will not be accepted as part of this Research Topic.