Automatic colonic fold segmentation for computed tomography colonography

2012 ◽  
Author(s):  
Hongbin Zhu ◽  
Matthew Barish ◽  
Lihong Li ◽  
Bowen Song ◽  
Donald Harrington ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Computed Tomography Colonography ◽  
Fold Segmentation

scholarly journals Computed Tomography Colonography as a Sensible Option for Colorectal Cancer Screening: Evidence Based on Metanalysis

2021 ◽  
Vol 41 (01) ◽  
pp. 087-095
Author(s):  
Ingrid Chaves de Souza Borges ◽  
Natália Costa Resende Cunha ◽  
Amanda Marsiaj Rassi ◽  
Marcela Garcia de Oliveira ◽  
Jacqueline Andréia Bernardes Leão-Cordeiro ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Computed Tomography ◽  
Cancer Screening ◽  
Literature Search ◽  
Web Of Science ◽  
Screening Method ◽  
Colorectal Polyp ◽  
Evidence Based ◽  
Polyp Detection ◽  
Computed Tomography Colonography

Abstract Objective This metanalysis aimed to evaluate the sensitivity and specificity of computed tomography colonography in colorectal polyp detection. Methods A literature search was performed in the PubMed and Web of Science databases. Results A total of 1,872 patients (males 57.2%, females 42.8%) aged 49 to 82 years old (mean age 59.7 ± 5.3 years) were included in this metanalysis. The estimated sensitivity of computed tomography colonography was 88.4% (46.3–95.7%, coefficient of variation [CV] = 28.5%) and the estimated specificity was 73.6% (47.4–100.0%, CV = 37.5%). For lesions up to 9 mm, the sensitivity was 82.5% (62.0–99.9%, CV = 25.1%) and the specificity was 79.2% (32.0–98.0%, CV = 22.9%). For lesions > 9 mm, the sensitivity was 90.2% (64.0–100.0%, CV = 7.4%) and the specificity was 94.7% (80.0–100.0%, CV = 6.2%). No statistically significant differences in sensitivity according to the size of the lesion were found (p = 0.0958); however, the specificity was higher for lesions > 9 mm (p < 0.0001). Conclusions Most of the studies analyzed in the present work were conducted before 2010, which is about a decade after computed tomography colonography started being indicated as a screening method by European and American guidelines. Therefore, more studies aimed at analyzing the technique after further technological advancements are necessary, which could lead to the development of more modern devices.

scholarly journals Diagnostic value of magnetic resonance versus computed tomography colonography for colorectal cancer

Medicine ◽  
2018 ◽  
Vol 97 (22) ◽  
pp. e10883
Author(s):  
Shuangyan Sun ◽  
Changliang Yang ◽  
Zhicheng Huang ◽  
Wen Jiang ◽  
Yan Liu ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Computed Tomography ◽  
Magnetic Resonance ◽  
Diagnostic Value ◽  
Computed Tomography Colonography

Colon capsule versus computed tomography colonography for colorectal cancer screening in patients with positive fecal occult blood test who refuse colonoscopy: a randomized trial

Endoscopy ◽  
2018 ◽  
Vol 50 (08) ◽  
pp. 761-769 ◽  
Author(s):  
Mathieu Pioche ◽  
Christell Ganne ◽  
Rodica Gincul ◽  
Antoine De Leusse ◽  
Julien Marsot ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Blood Test ◽  
Fecal Occult Blood Test ◽  
Screening Program ◽  
Occult Blood ◽  
Fecal Occult Blood ◽  
Occult Blood Test ◽  
Computed Tomography Colonography ◽  
Invitation Letter ◽  
Fecal Occult

Abstract Objective Some patients (10 % – 32 %) with a positive guaiac fecal occult blood test (gFOBT) do not undergo the recommended colonoscopy. The aim of this study was to compare video capsule endoscopy (VCE) and computed tomography colonography (CTC) in terms of participation rate and detection outcomes when offered to patients with a positive gFOBT who did not undergo the recommended colonoscopy. Methods An invitation letter offering CTC or VCE was sent to selected patients after randomization. Acceptance of the proposed (or alternative) procedure and procedure results were recorded. Sample size was evaluated according to the hypothesis of a 13 % increase of participation with VCE. Results A total of 756 patients were targeted. Following the invitation letter, 5.0 % (19/378) of patients underwent the proposed VCE and 7.4 % (28/378) underwent CTC, (P = 0.18). Following the letter, 9.8 % (37/378) of patients in the VCE group underwent a diagnostic procedure (19 VCE, 1 CTC, 17 colonoscopy) vs. 10.8 % in the CTC group (41/378: 28 CTC, 13 colonoscopy; P = 0.55). There were more potentially neoplastic lesions diagnosed in the VCE group than in the CTC group (12/20 [60.0 %] vs. 8/28 [28.6 %]; P = 0.04). Thus, 15/20 noninvasive procedures in the VCE group (19 VCE, 1 CTC; 75.0 %) vs. 10/28 in the CTC group (35.7 %; P = 0.01) resulted in a recommendation of further colonoscopy, but only 10/25 patients actually underwent this proposed colonoscopy. Conclusion Patients with a positive gFOBT result who do not undergo the recommended colonoscopy are difficult to recruit to the screening program and simply proposing an additional, less-invasive procedure, such as VCE or CTC, is not an effective strategy.

Multislice Computed Tomography Colonography: Technique Optimization

2001 ◽  
pp. 216-225
Author(s):  
Andrea Laghi ◽  
Valeria Panebianco ◽  
Carlo Catalano ◽  
Riccardo Iannaccone ◽  
Filippo G. Assael ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Multislice Computed Tomography ◽  
Computed Tomography Colonography ◽  
Technique Optimization

Machine Learning for Automated Polyp Detection in Computed Tomography Colonography

2012 ◽  
pp. 830-850
Author(s):  
Abhilash Alexander Miranda ◽  
Olivier Caelen ◽  
Gianluca Bontempi
Keyword(s):  
Machine Learning ◽  
Computed Tomography ◽  
False Positive ◽  
False Positive Rate ◽  
Learning Algorithms ◽  
Colorectal Polyps ◽  
Machine Learning Algorithms ◽  
Computed Tomography Colonography ◽  
Positive Rate ◽  
Independent Features

This chapter presents a comprehensive scheme for automated detection of colorectal polyps in computed tomography colonography (CTC) with particular emphasis on robust learning algorithms that differentiate polyps from non-polyp shapes. The authors’ automated CTC scheme introduces two orientation independent features which encode the shape characteristics that aid in classification of polyps and non-polyps with high accuracy, low false positive rate, and low computations making the scheme suitable for colorectal cancer screening initiatives. Experiments using state-of-the-art machine learning algorithms viz., lazy learning, support vector machines, and naïve Bayes classifiers reveal the robustness of the two features in detecting polyps at 100% sensitivity for polyps with diameter greater than 10 mm while attaining total low false positive rates, respectively, of 3.05, 3.47 and 0.71 per CTC dataset at specificities above 99% when tested on 58 CTC datasets. The results were validated using colonoscopy reports provided by expert radiologists.

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