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Chawla T, Hurrell C, Keough V, Lindquist CM, Mohammed MF, Samson C, Sugrue G, Walsh C. Canadian Association of Radiologists Practice Guidelines for Computed Tomography Colonography. Can Assoc Radiol J 2024; 75:54-68. [PMID: 37411043 DOI: 10.1177/08465371231182975] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/08/2023] Open
Abstract
Colon cancer is the third most common malignancy in Canada. Computed tomography colonography (CTC) provides a creditable and validated option for colon screening and assessment of known pathology in patients for whom conventional colonoscopy is contraindicated or where patients self-select to use imaging as their primary modality for initial colonic assessment. This updated guideline aims to provide a toolkit for both experienced imagers (and technologists) and for those considering launching this examination in their practice. There is guidance for reporting, optimal exam preparation, tips for problem solving to attain high quality examinations in challenging scenarios as well as suggestions for ongoing maintenance of competence. We also provide insight into the role of artificial intelligence and the utility of CTC in tumour staging of colorectal cancer. The appendices provide more detailed guidance into bowel preparation and reporting templates as well as useful information on polyp stratification and management strategies. Reading this guideline should equip the reader with the knowledge base to perform colonography but also provide an unbiased overview of its role in colon screening compared with other screening options.
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Affiliation(s)
- Tanya Chawla
- Department of Medical Imaging, University of Toronto, Toronto, Ontario, Canada
- Mount Sinai Hospital, Toronto, Ontario, Canada
| | - Casey Hurrell
- Canadian Association of Radiologists, Ottawa, Ontario, Canada
| | - Valerie Keough
- Department of Diagnostic Radiology, Dalhousie University, Halifax, Nova Scotia, Canada
| | - Chris M Lindquist
- Department of Radiology, University of Manitoba, Winnipeg, Manitoba, Canada
| | - Mohammed F Mohammed
- Abdominal Radiology Section, Department of Radiology, King Faisal Specialist Hospital and Research Centre, Riyadh, Saudi Arabia
| | - Caroline Samson
- Département de Radiologie, Radio-oncologie et Médecine Nucléaire, Université de Montréal, Montreal, Quebec, Canada
| | - Gavin Sugrue
- Department of Radiology, University of British Columbia, Vancouver, BC, Canada
| | - Cynthia Walsh
- Department of Radiology, Radiation Oncology and Medical Physics, University of Ottawa, Ottawa, Ontario, Canada
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Grosu S, Wiemker R, An C, Obmann MM, Wong E, Yee J, Yeh BM. Comparison of the performance of conventional and spectral-based tagged stool cleansing algorithms at CT colonography. Eur Radiol 2022; 32:7936-7945. [PMID: 35486170 DOI: 10.1007/s00330-022-08831-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2022] [Revised: 03/15/2022] [Accepted: 04/20/2022] [Indexed: 01/03/2023]
Abstract
OBJECTIVES To compare the performance of conventional versus spectral-based electronic stool cleansing for iodine-tagged CT colonography (CTC) using a dual-layer spectral detector scanner. METHODS We retrospectively evaluated iodine contrast stool-tagged CTC scans of 30 consecutive patients (mean age: 69 ± 8 years) undergoing colorectal cancer screening obtained on a dual-layer spectral detector CT scanner. One reader identified locations of electronic cleansing artifacts (n = 229) on conventional and spectral cleansed images. Three additional independent readers evaluated these locations using a conventional cleansing algorithm (Intellispace Portal) and two experimental spectral cleansing algorithms (i.e., fully transparent and translucent tagged stool). For each cleansed image set, readers recorded the severity of over- and under-cleansing artifacts on a 5-point Likert scale (0 = none to 4 = severe) and readability compared to uncleansed images. Wilcoxon's signed-rank tests were used to assess artifact severity, type, and readability (worse, unchanged, or better). RESULTS Compared with conventional cleansing (66% score ≥ 2), the severity of overall cleansing artifacts was lower in transparent (60% score ≥ 2, p = 0.011) and translucent (50% score ≥ 2, p < 0.001) spectral cleansing. Under-cleansing artifact severity was lower in transparent (49% score ≥ 2, p < 0.001) and translucent (39% score ≥ 2, p < 0.001) spectral cleansing compared with conventional cleansing (60% score ≥ 2). Over-cleansing artifact severity was worse in transparent (17% score ≥ 2, p < 0.001) and translucent (14% score ≥ 2, p = 0.023) spectral cleansing compared with conventional cleansing (9% score ≥ 2). Overall readability was significantly improved in transparent (p < 0.001) and translucent (p < 0.001) spectral cleansing compared with conventional cleansing. CONCLUSIONS Spectral cleansing provided more robust electronic stool cleansing of iodine-tagged stool at CTC than conventional cleansing. KEY POINTS • Spectral-based electronic cleansing of tagged stool at CT colonography provides higher quality images with less perception of artifacts than does conventional cleansing. • Spectral-based electronic cleansing could potentially advance minimally cathartic approach for CT colonography. Further clinical trials are warranted.
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Affiliation(s)
- Sergio Grosu
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, 513 Parnassus Avenue, San Francisco, CA, 94143, USA. .,Department of Radiology, University Hospital, LMU Munich, Marchioninistraße 15, 81377, Munich, Germany.
| | - Rafael Wiemker
- Philips Research Laboratories Hamburg, Röntgenstraße 24, 22335, Hamburg, Germany
| | - Chansik An
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, 513 Parnassus Avenue, San Francisco, CA, 94143, USA
| | - Markus M Obmann
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, 513 Parnassus Avenue, San Francisco, CA, 94143, USA.,Department of Radiology and Nuclear Imaging, University Hospital Basel, Petersgraben 4, CH - 4051, Basel, Switzerland
| | - Eddy Wong
- CT/AMI Clinical Science, Philips Healthcare, 100 Park Avenue, Orange Village, OH, 44122, USA
| | - Judy Yee
- Department of Radiology, Montefiore Medical Center, Albert Einstein College of Medicine, 111 E 210th Street, Bronx, NY, 10467-2401, USA
| | - Benjamin M Yeh
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, 513 Parnassus Avenue, San Francisco, CA, 94143, USA
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Tachibana R, Näppi JJ, Hironaka T, Yoshida H. Self-Supervised Adversarial Learning with a Limited Dataset for Electronic Cleansing in Computed Tomographic Colonography: A Preliminary Feasibility Study. Cancers (Basel) 2022; 14:4125. [PMID: 36077662 PMCID: PMC9454562 DOI: 10.3390/cancers14174125] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2022] [Revised: 08/15/2022] [Accepted: 08/18/2022] [Indexed: 11/17/2022] Open
Abstract
Existing electronic cleansing (EC) methods for computed tomographic colonography (CTC) are generally based on image segmentation, which limits their accuracy to that of the underlying voxels. Because of the limitations of the available CTC datasets for training, traditional deep learning is of limited use in EC. The purpose of this study was to evaluate the technical feasibility of using a novel self-supervised adversarial learning scheme to perform EC with a limited training dataset with subvoxel accuracy. A three-dimensional (3D) generative adversarial network (3D GAN) was pre-trained to perform EC on CTC datasets of an anthropomorphic phantom. The 3D GAN was then fine-tuned to each input case by use of the self-supervised scheme. The architecture of the 3D GAN was optimized by use of a phantom study. The visually perceived quality of the virtual cleansing by the resulting 3D GAN compared favorably to that of commercial EC software on the virtual 3D fly-through examinations of 18 clinical CTC cases. Thus, the proposed self-supervised 3D GAN, which can be trained to perform EC on a small dataset without image annotations with subvoxel accuracy, is a potentially effective approach for addressing the remaining technical problems of EC in CTC.
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Affiliation(s)
- Rie Tachibana
- 3D Imaging Research, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, 25 New Chardon Street, Suite 400C, Boston, MA 02114, USA
- Information Science & Technology Department, National Institute of Technology, Oshima College, 1091-1 Komatsu Suo-Oshima, Oshima, Yamaguchi 742-2193, Japan
| | - Janne J. Näppi
- 3D Imaging Research, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, 25 New Chardon Street, Suite 400C, Boston, MA 02114, USA
| | - Toru Hironaka
- 3D Imaging Research, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, 25 New Chardon Street, Suite 400C, Boston, MA 02114, USA
| | - Hiroyuki Yoshida
- 3D Imaging Research, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, 25 New Chardon Street, Suite 400C, Boston, MA 02114, USA
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