The CT colonography radiology and data system: history, updates, and future directions.
Authors
Affiliations (3)
Affiliations (3)
- Department of Radiology, Boston University Chobanian & Avedisian School of Medicine, 820 Harrison Ave, FGH 4001, Boston, MA, USA.
- Department of Radiology and Imaging Sciences, Emory University School of Medicine, 1364 Clifton Road, Atlanta, Georgia.
- Department of Radiology, Boston University Chobanian & Avedisian School of Medicine, 820 Harrison Ave, FGH 4001, Boston, MA, USA. [email protected].
Abstract
Computed tomography colonography, also known as virtual colonoscopy, is a minimally invasive imaging technique developed in the early 1990s to evaluate the colon for polyps, cancer, and other abnormalities. Advances in multidetector computed tomography, bowel preparation protocols, and three-dimensional reconstruction rapidly improved diagnostic performance. Landmark trials demonstrating sensitivity for clinically significant adenomas comparable to optical colonoscopy led to its acceptance as a colorectal cancer screening option. Standardized reporting further accelerated clinical uptake. The CT Colonography Reporting and Data System (C-RADS), introduced in 2005, provided a structured lexicon for colonic and extracolonic findings, allowing more consistent communication and management. Prior to structured reporting, early clinical implementation of computed tomography colonography was challenged by wide variability in terminology and follow-up thresholds. C-RADS addressed these issues by categorizing colonic findings according to adequacy of evaluation and likelihood of neoplastic disease and stratifying extracolonic observations based on clinical significance. The system enhanced clarity, increased reproducibility, and supported longitudinal research efforts by enabling systematic outcome tracking. Experience gained over the next decade and a half led to the C-RADS 2023 update. Notable changes included subdivision of intermediate colonic findings to distinguish likely benign diverticular-associated soft tissue thickening from indeterminate lesions, and simplification of extracolonic categories to reduce over-reporting of clinically unimportant findings. Future directions include broader adoption across health systems, automated detection and computer-assisted interpretation, and integration with artificial intelligence to improve polyp identification and extracolonic assessment. Together, these advances are expected to expand access, improve efficiency, and further solidify computed tomography colonography within evidence-supported screening practice.