Computerized radiology of the colon: A potential screening technique

Coin, Carl G.; Wollett, Fredric C.; Coin, J. Thaddeus; Rowland, Michael L.; Deramos, Rafael K.; Dandrea, Richard

doi:10.1016/0730-4862(83)90145-2

Cited by 47 publications

(4 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Colonoscopy is used both as a primary screening tool and as follow-up for individuals testing positive with other screening methods. In addition, computed tomographic (CT) colonography, an imaging method based on scanning technology, has been developed as a less invasive visualization technique for colorectal cancer screening 14 . Newer techniques, which have recently emerged but have not been widely tested yet, are based on either visual inspection (e.g.…”

mentioning

confidence: 99%

The IARC Perspective on Colorectal Cancer Screening

et al. 2018

View full text Add to dashboard Cite

mentioning

confidence: 99%

The IARC Perspective on Colorectal Cancer Screening

et al. 2018

View full text Add to dashboard Cite

“…Virtual colonoscopy (VC) or computed tomography colonography (CTC) is one of the most acceptable techniques for non-invasive screening of colon cancer [ 1 – 5 ]. Colon cancer is the second common cause of cancer death in the United States [ 6 ], whereas the second cause of cancer death in Thailand is colon and rectum cancer [ 7 ].…”

Section: Introductionmentioning

confidence: 99%

Electronic cleansing in computed tomography colonography using AT layer identification with integration of gradient directional second derivative and material fraction model

et al. 2017

View full text Add to dashboard Cite

Background:In computed tomography colonography images, electronic cleansing (EC) is applied to remove opacified residual materials, called fecal-tagging materials (FTM), using positive-contrast tagging agents and laxative to facilitate polyp detection. Methods:The proposed EC, EC prop , integrates the gradient directional second derivative into material fraction model to preserve submerged soft tissue (ST) under FTM. Three-material fraction model is used to remove FTM and artifacts at air-tagging (AT) layers and T-junctions where air, ST, and FTM material meet simultaneously. Moreover, the proposed AT layer identification is used to distinguish AT layers from air-tissue-tagging (ATT) layers in order to preserve ATT layers during cleansing. The clinical evaluation on 467 3-Dimensional band view images was conducted by the abdominal radiologist using four grading levels of cleansing quality with five causes of low quality EC. The amount of the remaining artifacts at T-junctions was approximated from the results of EC prop . The results from EC prop were compared with the results from syngo.via Client 3.0 Software, EC syngo , and the fast three-material modeling, EC prev , using the preference of the radiologist. Two-tailed paired Wilcoxon signed rank test is used to indicate statistical significance. Results: The average grade on cleansing quality is 2.89 out of 4. The artifacts at T-junctions from 86.94% of the test images can be removed, whereas artifacts at T-junctions from only 13.06% of the test images cannot be removed. For 13.06% of the test images, the results from EC prop are more preferable to the results from EC syngo (p < 0.008). For all the test images, the results from EC prop are more preferable to the results from EC prev (p < 0.001). Finally, the visual assessment shows that EC prop can preserve ATT layers, submerged polyps and folds while EC prev can preserve only submerged folds but fails to preserve ATT layers.

show abstract

“…Therefore, medical centers are seeking al-ternative techniques as population screening tools. CT colonography ͑CTC͒, also known as virtual colonoscopy, has been proposed as an alternative, less invasive technique for detecting colorectal neoplasms, [3][4][5][6] which has less examination time and less patient discomfort. However, the sensitivity of CTC can be lower for inexperienced readers because there is a long learning curve with CTC reading.…”

Section: Introductionmentioning

confidence: 99%