FUTURE TRENDS
In the oncologic setting, CT and MRI play a pivotal role in not only
providing the diagnosis and information on disease burden but also
evaluating treatment response and imaging surveillance. However,
conventional CT and MRI techniques occasionally have limitations in
differentiating between different types of tumors that may occur in the
same location or differentiating between treatment-related changes and
viable tumor in the posttreatment setting. In addition, they do not
provide detail regarding tumor histoarchitecture and physiology or
imaging parameters that can be used for risk stratification. As a
result, over past decades, there has been great effort in developing
advanced imaging techniques that can address these formidable challenges35,40-42.
Dual-energy CT allows acquisition of images simultaneously at high- and
low-energy spectra simultaneously with radiation doses that is equal to
or less than the conventional single-energy CT. Virtual noncontrast
images can be generated from dual-energy CT dataset reducing acquisition
time and radiation 43.
Iodine concentration in the tumor can also be assessed qualitatively and
quantitatively. This may help in tumor delineation and separation
between residual viable tumor and treatment fibrosis44.
There are several advanced MRI techniques that are used for imaging of
tumors in the skull base and head and neck region, such as
high-resolution 3D MRI, DWI, MR perfusion, and MR spectroscopy. These
techniques have demonstrated a wide range of potential utilities in
diagnosis, tumor prognostication and posttreatment evaluation35,40,41.
Moreover, newer MR technology such as fast MRI sequences can reduce the
scan time which is particularly useful in pediatric population as it can
minimize motion artifact and decrease sedation needs45. Furthermore, zero
echo time (TE) sequences, so called black bone MRI, may show promise in
bone evaluation due to its high soft tissue/ bone contrast reducing the
need for CT46,47.
More scientific data and research are needed to evaluate the efficacy of
these advanced techniques in clinical practice.
Finally, with the advent of powerful processing capabilities, artificial
intelligence in radiology (radiomics) will allow for extraction of
quantifiable data from imaging furthering tumor and treatment imaging
phenotype understanding. Combining radiomics and genomics, so called
radiogenomics, may aid in tumor behavioral understanding and risk
stratification and prognostication.