Computed Tomography Advances in Oncoimaging
Section snippets
CT Advances in Oncology
Oncology remains among the most rapidly evolving medical branches, with innumerable medical and technical advances in therapy happening every year. With the advent of molecular genomics and targeted therapy, we have now entered the era of personalized medicine and personalized radiology in oncology. Indeed, radiologists are now an integral part of the tumor board along with oncosurgeons, medical and radiation oncologists, and pathologists. In the past decade or 2, there has been a rapidly
Computer-Aided Diagnosis
The initial iteration of what has now transformed into one of the most exciting and promising advances in CT diagnostics was “computer-aided diagnosis” (CAD), a term first employed in the scientific literature in 19661 by Lodwick. CAD has been broadly defined as “the use of computer algorithms to aid the image interpretation process”. An ideal CAD should be one which improves study sensitivity without compromising on specificity, improves reading times, integrates seamlessly with the workflow,
CT Volumetry
Although unidimensional or bidimensional change in tumor size is the standard technique for evaluating tumor response as per RECIST (Response Evaluation Criteria in Solid Tumors) and WHO (World Health Organization) criteria respectively, variation in tumor size by diameter does not always accurately reflect disease response as tumors may not grow or shrink uniformly in all directions. Isotropic data acquisition in multidetector CT permits tumor quantification by volumetry, which provides a more
Arterial Enhancement Fraction
Arterial enhancement fraction (AEF) is a postprocessing technique performed on already acquired multiphasic liver CT scans for the purpose of quantitative color mapping (Figure 4) of degree of enhancement. It is the ratio of enhancement on arterial phase to the enhancement on portal phase with respect to noncontrast images and is determined as [HU(ap) − HU (nc)/HU(pp) − HU(nc) × 100] where HU is Hounsfield unit, ap arterial phase, and nc noncontrast and pp – portal phase.
Kim et al51 reported
Dedicated Software For Assessing Tumor Burden in Clinical Trials
Assessment of treatment response, particularly in trial settings, depends on objective measurement of the tumor burden using tumor response criteria like RECIST 1.1. These criteria depend on measuring the sum of target lesion diameters and comparing with previous imaging. Besides being relatively time consuming, the mathematical component also has an inherent risk of human error, be it because of rounding up target lesion sizes or calculation errors.54, 55 Multiple software options (Figure 6)
CT Colonography
Since its first application in 1994,58 CTC or virtual colonoscopy has gained popularity as a colorectal cancer screening tool worldwide in addition to being a prevalent diagnostic test for colonic pathologies. High sensitivity, specificity, and positive and negative predictive values of 0.90, 0.86, 0.23, and 0.99, respectively were reported in the National CT colonography trial conducted by the American College of Radiology Imaging Network (ACRIN)59 in 2008.
The CTC technique60 comprises of
Cone-Beam Breast CT
Although initially available in the 1970s when breast CT was not comparable with conventional mammography, recent technological advances such as flat-panel detectors and a cone-beam geometry in CT scanners have led to a rekindled interest in breast CT.67 Current cone-beam breast CT (CBCT) scanners consist of a tungsten-anode X-ray tube (collimated to a half-cone geometry) which rotates 360° around a vertical axis. The patient lies prone with the breast to be imaged placed into a depression in
Conclusion
In this article, we discussed a few of the recent advances in CT and postprocessing techniques, which have the potential to change the way we practice radiology at present. The future oncoradiologist will be harnessing the enormous potential of computational analytics, postprocessing softwares, and machine learning to enable faster and better diagnosis. At the same time, they will need to balance their various potential uses and select what is most relevant and clinically meaningful for making
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Computed Tomography Image Features under Deep Learning Algorithm Applied in Staging Diagnosis of Bladder Cancer and Detection on Ceramide Glycosylation
2022, Computational and Mathematical Methods in Medicine