Machine Learning-Based Differentiation of Benign and Premalignant Colorectal Polyps Detected with CT Colonography in an Asymptomatic Screening Population: A Proof-Of-Concept Study
Abstract
Background CT colonography does not enable definite differentiation between benign and premalignant colorectal polyps. Purpose To perform machine learning–based differentiation of benign and premalignant colorectal polyps detected with CT colonography in an average-risk asymptomatic colorectal cancer screening sample with external validation using radiomics. Materials and Methods In this secondary analysis of a prospective trial, colorectal polyps of all size categories and morphologies were manually segmented on CT colonographic images and were classified as benign (hyperplastic polyp or regular mucosa) or premalignant (adenoma) according to the histopathologic reference standard. Quantitative image features characterizing shape (n = 14), gray level histogram statistics (n = 18), and image texture (n = 68) were extracted from segmentations after applying 22 image filters, resulting in 1906 feature-filter combinations. Based on these features, a random forest classification algorithm was trained to predict the individual polyp character. Diagnostic performance was validated in an external test set. Results The random forest model was fitted using a training set consisting of 107 colorectal polyps in 63 patients (mean age, 63 years ± 8 [standard deviation]; 40 men) comprising 169 segmentations on CT colonographic images. The external test set included 77 polyps in 59 patients comprising 118 segmentations. Random forest analysis yielded an area under the receiver operating characteristic curve of 0.91 (95% CI: 0.85, 0.96), a sensitivity of 82% (65 of 79) (95% CI: 74%, 91%), and a specificity of 85% (33 of 39) (95% CI: 72%, 95%) in the external test set. In two subgroup analyses of the external test set, the area under the receiver operating characteristic curve was 0.87 in the size category of 6–9 mm and 0.90 in the size category of 10 mm or larger. The most important image feature for decision making (relative importance of 3.7%) was quantifying first-order gray level histogram statistics. Conclusion In this proof-of-concept study, machine learning–based image analysis enabled noninvasive differentiation of benign and premalignant colorectal polyps with CT colonography. © RSNA, 2021 Online supplemental material is available for this article.
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Radiology is one step ahead and shows what is possible with the emerging field of radiomics-the extraction of data from radiograms to diagnose cancer, predict outcomes or guide therapy.
“Radiology is one step ahead and shows what is possible with the emerging field of radiomics—the extraction of data from radiograms to diagnose cancer, predict outcomes or guide therapy (54–56).”
From Artificial Intelligence: Present and Future Potential for Solid Organ Transplantation (2022) · cites this paper as [54]
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The Adenoma-Carcinoma Sequence, Management, and Treatment of Colon Cancer
Bortz, J. H., & Friedrich-Nel, H. (2023) · Springer eBooks · doi:10.1007/978-3-031-30866-6_15
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Multi-View Network for Colorectal Polyps Detection in CT Colonography
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Emerging Role of Artificial Intelligence in Colorectal Cancer: Screening and Diagnosis
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Computer-Assisted Analysis of Colonoscopy
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- Authors
- Grosu, S., Wesp, P., Graser, A., Maurus, S., Schulz, C., Knösel, T., Cyran, C. C., Ricke, J., Ingrisch, M., & Kazmierczak, P. M.
- Affiliations
- LMU Klinikum (DE)
- ORCID
- Venue
- Radiology
- Published
- 2021-05
- Volume
- 299
- Issue
- 2
- Pages
- 326-335
- ISSN
- 0033-8419 (ISSN-L)
- 1527-1315
- OpenAlex
- W3131887757
- Cited by
- 75
- References
- 42
- Citations by year
- 2026: 12 · 2025: 13 · 2024: 19 · 2023: 19 · 2022: 9 · 2021: 3
- FWCI
- 6.59
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Radiology (publishedVersion)
- PubMed (publishedVersion)
- Funders
- Ludwig-Maximilians-Universität München
- Topics
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Radiomics and Machine Learning in Medical Imaging
- Health Sciences › Medicine › Oncology › Colorectal Cancer Screening and Detection
- Physical Sciences › Computer Science › Artificial Intelligence › AI in cancer detection
- Keywords
- colorectal polyps, CT colonography, radiomics, colorectal adenoma, hyperplastic polyps, random forest classification, image texture features, polyp size, colorectal cancer screening, non-invasive diagnosis, external validation, image segmentation, image filters, polyp morphology
- MeSH terms
- Machine Learning, Proof of Concept Study, Aged, Colonic Neoplasms, Colonic Polyps, Contrast Media, Diagnosis, Differential, Female, Humans, Image Interpretation, Computer-Assisted, Male, Mass Screening, Middle Aged, Precancerous Conditions, Prospective Studies, Colonography, Computed Tomographic
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