ArticleThe British journal of radiology2011
CT colonography: computer-assisted detection of colorectal cancer.
Article in The British journal of radiology, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 1 of them a synthesis that pooled it.
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Who cites it
9 citing papers in PubMed, 1 synthesis or guideline pooled it, 24 citations in OpenAlex.
- Guideline for referral of patients with suspected colorectal cancer by family physicians and other primary care providers.Canadian family physician Medecin de famille canadien · 2014Pooled it
- Trial
- Signaling pathways involved in colorectal cancer: pathogenesis and targeted therapy.Signal transduction and targeted therapy · 2024Review
- A narrative review on current imaging applications of artificial intelligence and radiomics in oncology: focus on the three most common cancers.La Radiologia medica · 2022Review
- Emerging methods for prostate cancer imaging: evaluating cancer structure and metabolic alterations more clearly.Molecular oncology · 2021Review
- Appearances of screen-detected versus symptomatic colorectal cancers at CT colonography.European radiology · 2016Article
- Model-based vs hybrid iterative reconstruction technique in ultralow-dose submillisievert CT colonography.The British journal of radiology · 2015Article
- Logistic regression model for diagnosis of transition zone prostate cancer on multi-parametric MRI.European radiology · 2015Article
- Automated image-based colon cleansing for laxative-free CT colonography computer-aided polyp detection.Medical physics · 2011Article
Corrections and comments
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Authors and funding
7 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectivesComputer-aided detection (CAD) for CT colonography (CTC) has been developed to detect benign polyps in asymptomatic patients. We aimed to determine whether such a CAD system can also detect cancer in symptomatic patients.
methodsCTC data from 137 symptomatic patients subsequently proven to have colorectal cancer were analysed by a CAD system at 4 different sphericity settings: 0, 50, 75 and 100. CAD prompts were classified by an observer as either true-positive if overlapping a cancer or false-positive if elsewhere. Colonoscopic data were used to aid matching.
resultsOf 137 cancers, CAD identified 124 (90.5%), 122 (89.1%), 119 (86.9%) and 102 (74.5%) at a sphericity of 0, 50, 75 and 100, respectively. A substantial proportion of cancers were detected on either the prone or supine acquisition alone. Of 125 patients with prone and supine acquisitions, 39.3%, 38.3%, 43.2% and 50.5% of cancers were detected on a single acquisition at a sphericity of 0, 50, 75 and 100, respectively. CAD detected three cancers missed by radiologists at the original clinical interpretation. False-positive prompts decreased with increasing sphericity value (median 65, 57, 45, 24 per patient at values of 0, 50, 75, 100, respectively) but many patients were poorly prepared.
conclusionCAD can detect symptomatic colorectal cancer but must be applied to both prone and supine acquisitions for best performance.
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