A method to discriminate different types of renal cell carcinoma (RCC) was developed using attenuation values observed in multiphasic contrast-enhanced CT. This work evaluates the sensitivity of this RCC discrimination task at different CT radiation dose levels.
We selected 5 cases of kidney lesion patients who had undergone four-phase CT scans covering the abdomen to the lilac crest. Through an IRB-approved study, the scans were conducted on 64-slice CT scanners (Definition AS/Definition Flash, Siemens Healthcare) using automatic tube-current modulation (TCM). The protocol included an initial baseline unenhanced scan, followed by three post-contrast injection phases. CTDIvol (32 cm phantom) measured between 9 to 35 mGy for any given phase. As a preliminary study, we limited the scope to the cortico-medullary phase-shown previously to be the most discriminative phase. A previously validated method was used to simulate a reduced dose acquisition via adding noise to raw CT sinogram data, emulating corresponding images at simulated doses of 50%, 25%, and 10%. To discriminate the lesion subtype, ROIs were placed in the most enhancing region of the lesion. The mean HU value of an ROI was extracted and used to discriminate to the worst-case RCC subtype, ranked in the order of clear cell, papillary, chromophobe and the benign oncocytoma.
Two patients exhibited a change of worst case RCC subtype between original and simulated scans, at 25% and 10% doses. In one case, the worst-case RCC subtype changed from oncocytoma to chromophobe at 10% and 25% doses, while the other case changed from oncocytoma to clear cell at 10% dose.
Based on preliminary results from an initial cohort of 5 patients, worst-case RCC subtypes remained constant at all simulated dose levels except for 2 patients. Further study conducted on more patients will be needed to confirm our findings. Institutional research agreement, Siemens Healthcare; Past recipient, research grant support, Siemens Healthcare; Consultant, Toshiba America Medical Systems; Consultant, Samsung Electronics; NIH Grant Support from: U01 CA181156.
Medical physics. 2016 Jun [Epub]
M Wahi-Anwar, S Young, P Lo, S Raman, H Kim, M Brown, M McNitt-Gray, H Coy, D Ashen-Garry, E Pace-Soler
UCLA School of Medicine, Los Angeles, CA.