The Value of “Liver Windows” Settings in the Detection of Small Renal Cell Carcinomas on Unenhanced Computed Tomography

Abstract

Use of liver windows settings improves the detection of small RCCs on the unenhanced CT.

Renal cancer at unenhanced CT: imaging features, detection rates, and outcomes

“…Our study was of patients encountered during routine clinical practice undergoing unenhanced CT for a multitude of clinical indications, where the prevalence of incidental renal cancer was less than 1% of patients. Thus our detection rate of 63% is likely more realistic than those based on enriched cohorts which had renal cancer rates as high as 50% (14,15). Finally, as radiologists in our cohort were challenged with not only perceiving renal cancers, but also differentiating them from benign renal masses, our detection rate is likely more accurate than studies which had excluded non-malignant renal findings such as cysts (14,15).…”

“…This highlights the need to visualize attenuation differences between a possible renal cancer and the adjacent normal renal parenchyma. This can be aided by using narrow window settings (15). Also, renal cancers which deformed the outer renal contour were more likely to be detected at unenhanced CT than those that did not (14).…”

“…Studies have shown that renal cancers can be detected at unenhanced CT due to a difference in attenuation relative to normal renal parenchyma or due to a deformation of the renal contour (14). In addition, use of narrow window settings may improve renal mass detection (15). As these prior studies utilized preselected cohorts of renal cancers, they cannot be used to determine the detection rate of renal cancer in clinical practice.…”

Using Correlative Properties of Neighboring Pixels to Improve Gray-White Differentiation in Pediatric Head CT Images

“…20 Radiologists routinely adjust the window and level of digital radiologic images to optimize the contrast needed for easy visualization of details of interest such as GWD. [24][25][26][27] Such contrast optimization, however, does not affect the relative intensity values assigned to individual pixels on the Hounsfield unit scale and would not be expected to improve the inherent CNR between gray and white matter. On the other hand, our image-processing algorithm improved the GWD by modifying relative assigned values of individual pixel intensity for pixels representing gray and white matter as indicated by improved CNR.…”

Minimising the impact of errors in the interpretation of CT images for surveillance and evaluation of therapy in cancer

“…However, it is surprising in the modern PACS era that we still see reporters relying on a single soft tissue window to review the whole body viscera. There is good evidence that changing windows in oncology scans is useful 9,10 and at a minimum the body should be reviewed using 5 window settings: soft tissue, lung, bone, liver and vascular 11 . The actual window level and width used will vary depending on slice thickness, exposure factors and presence of contrast media.…”