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.