Volume rendering with color coding of tagged stool during endoluminal fly-through CT colonography: effect on reading efficiency

CT Colonography Reporting and Data System (C-RADS): Version 2023 Update

The CT Colonography Reporting and Data System (C-RADS) has withstood the test of time and proven to be a robust classification scheme for CT colonography (CTC) findings. C-RADS version 2023 represents an update on the scheme used for colorectal and extracolonic findings at CTC. The update provides useful insights gained since the implementation of the original system in 2005. Increased experience has demonstrated confusion on how to classify the mass-like appearance of the colon consisting of soft tissue attenuation that occurs in segments with acute or chronic diverticulitis. Therefore, the update introduces a new subcategory, C2b, specifically for mass-like diverticular strictures, which are likely benign. Additionally, the update simplifies extracolonic classification by combining E1 and E2 categories into an updated extracolonic category of E1/E2 since, irrespective of whether a finding is considered a normal variant (category E1) or an otherwise clinically unimportant finding (category E2), no additional follow-up is required. This simplifies and streamlines the classification into one category, which results in the same management recommendation.

Improving Image Correlation and Differentiation of 3D Endoluminal Lesions in the Air Spaces Using a Novel Target Gray Level Mapping Technique: A Preliminary Study of Its Application to Computed Tomographic Colonography and Comparison with Traditional Surface Rendering Method

Purpose

To improve the three dimensional (3D) and two dimensional (2D) image correlation and differentiation of 3D endoluminal lesions in the traditional surface rendering (SR) computed tomographic endoscopy (CTE), a target gray level mapping (TGM) technique is developed and applied to computed tomographic colonography (CTC) in this study.

Methods

A study of sixty-two various endoluminal lesions from thirty patients (13 males, 17 females; age range 31–90 years) was approved by our institutional review board and evaluated retrospectively. The endoluminal lesions were segmented using gray level threshold. The marching cubes algorithm was used to detect isosurfaces in the segmented volumetric data sets. TGM allows users to interactively apply grey level mapping (GM) to region of interest (ROI) in the 3D CTC. Radiologists conducted the clinical evaluation and the resulting data were analyzed.

Results

TGM and GM are significantly superior to SR in terms of surface texture, 3D shape, the confidence of 3D to 2D, 2D to 3D image correlation, and clinical classification of endoluminal lesions (P < 0.01). The specificity and diagnostic accuracy of GM and TGM methods are significantly better than those of SR (P < 0.01). Moreover, TGM performs better than GM (specificity: 75.0–85.7% vs. 53.6–64.3%; accuracy: 88.7–93.5% vs. 77.4–83.9%). TGM is a preferable display mode for further localization and differentiation of a lesion in CTC navigation.

Conclusions

Compared with only the spatial shape information in traditional SR of CTC images, the 3D shapes and gray level information of endoluminal lesions can be provided by TGM simultaneously. 3D to 2D image correlations are also increased and facilitated at the same time. TGM is less affected by adjacent colon surfaces than GM. TGM serves as a better way to improve the image correlation and differentiation of endoluminal lesions.

CT colonography interpretation: how to maximize polyp detection and minimize overcalling

This article outlines how to achieve maximum accuracy in interpreting CT colonography (CTC) regarding colonic findings. Interpreting extracolonic findings seen on CTC is a separate diagnostic task and will not be addressed in this article. While many interpretive pitfalls are in fact related to CTC techniques, this article focuses on issues that are related to interpretive knowledge and skills, avoiding in-depth discussions on CTC techniques. Principal methods and further tips for detecting possible polyp candidates and for confirming true soft-tissue polyps will be discussed. Specific points about optimizing interpretation strategies for difficult flat polyps including sessile serrated polyp will be raised. There are numerous interpretive pitfalls regarding the colonic interpretation of CTC. Knowledge of these pitfalls will shorten the learning curve and help achieve accurate reads.

Strategies for improved interpretation of computer-aided detections for CT colonography utilizing distributed human intelligence

Computer-aided detection (CAD) systems have been shown to improve the diagnostic performance of CT colonography (CTC) in the detection of premalignant colorectal polyps. Despite the improvement, the overall system is not optimal. CAD annotations on true lesions are incorrectly dismissed, and false positives are misinterpreted as true polyps. Here, we conduct an observer performance study utilizing distributed human intelligence in the form of anonymous knowledge workers (KWs) to investigate human performance in classifying polyp candidates under different presentation strategies. We evaluated 600 polyp candidates from 50 patients, each case having at least one polyp ≥6 mm, from a large database of CTC studies. Each polyp candidate was labeled independently as a true or false polyp by 20 KWs and an expert radiologist. We asked each labeler to determine whether the candidate was a true polyp after looking at a single 3D-rendered image of the candidate and after watching a video fly-around of the candidate. We found that distributed human intelligence improved significantly when presented with the additional information in the video fly-around. We noted that performance degraded with increasing interpretation time and increasing difficulty, but distributed human intelligence performed better than our CAD classifier for "easy" and "moderate" polyp candidates. Further, we observed numerous parallels between the expert radiologist and the KWs. Both showed similar improvement in classification moving from single-image to video interpretation. Additionally, difficulty estimates obtained from the KWs using an expectation maximization algorithm correlated well with the difficulty rating assigned by the expert radiologist. Our results suggest that distributed human intelligence is a powerful tool that will aid in the development of CAD for CTC.

Efficacy of barium-based fecal tagging for CT colonography: a comparison between the use of high and low density barium suspensions in a Korean population - a preliminary study

OBJECTIVE

This preliminarily study was designed to determine and to compare the efficacy of two commercially available barium-based fecal tagging agents for CT colonography (CTC) (high-density [40% w/v] and low-density [4.6% w/v] barium suspensions) in a population in Korea.

MATERIALS AND METHODS

In a population with an identified with an average-risk for colorectal cancer, 15 adults were administered three doses of 20 ml 40% w/v barium for fecal tagging (group I) and 15 adults were administered three doses of 200 ml 4.6% w/v barium (group II) for fecal tagging. Excluding five patients in group I and one patient in group II that left the study, ten patients in group I and 14 patients in group II were finally included in the analysis. Two experienced readers evaluated the CTC images in consensus regarding the degree of tagging of stool pieces 6 mm or larger. Stool pieces were confirmed with the use of standardized CTC criteria or the absence of matched lesions as seen on colonoscopy. The rates of complete fecal tagging were analyzed on a per-lesion and a per-segment basis and were compared between the patients in the two groups.

RESULTS

Per-lesion rates of complete fecal tagging were 52% (22 of 42; 95% CI, 37.7-66.6%) in group I and 78% (28 of 36; 95% CI, 61.7-88.5%) in group II. The difference between the two groups did not reach statistical significance (p = 0.285). The per-segment rates of complete tagging were 33% (6 of 18; 95% CI, 16.1%-56.4%) in group I and 60% (9 of 15; 95% CI, 35.7%-80.3%) in group II; again, the difference between the two groups did not reach statistical significance (p = 0.171).

CONCLUSION

Barium-based fecal tagging using both the 40% w/v and the 4.6% w/v barium suspensions showed moderate tagging efficacy. The preliminary comparison did not demonstrate a statistically significant difference in the tagging efficacy between the use of the two tagging agents, despite the tendency toward better tagging with the use of the 4.6% w/v barium suspension.