Diagnostic Performance of Low-Dose CT for Acute Abdominal Conditions
DETECT_Acute
Diagnostic Performance of Deep Learning Image Reconstruction in Low Dose CT for the Detection of Acute Abdominal Conditions
3 other identifiers
observational
246
2 countries
2
Brief Summary
The goal of this non-inferiority observational study is to assess the diagnostic performance of low-dose CT with deep learning image reconstruction (DLIR) in adult participants with acute abdominal conditions. The main research question is: • Can low-dose CT with DLIR achieve the same diagnostic performance as standard CT for the diagnosis of acute abdominal conditions. Participants will be examined with an additional low-dose CT directly after the standard CT. Participant will be their own controls.
Trial Health
Trial Health Score
Automated assessment based on enrollment pace, timeline, and geographic reach
participants targeted
Target at P75+ for all trials
Started Dec 2022
Shorter than P25 for all trials
2 active sites
Health score is calculated from publicly available data and should be used for screening purposes only.
Trial Relationships
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Study Timeline
Key milestones and dates
First Submitted
Initial submission to the registry
November 15, 2022
CompletedStudy Start
First participant enrolled
December 7, 2022
CompletedFirst Posted
Study publicly available on registry
December 15, 2022
CompletedPrimary Completion
Last participant's last visit for primary outcome
July 10, 2023
CompletedStudy Completion
Last participant's last visit for all outcomes
July 10, 2023
CompletedOctober 17, 2023
October 1, 2023
7 months
November 15, 2022
October 16, 2023
Conditions
Keywords
Outcome Measures
Primary Outcomes (1)
Diagnostic performance of low-dose CT
Diagnostic performance of low-dose CT compared to standard CT according to ICD 10 diagnosis. Diagnostic performance measured in terms of: Sensitivity given in % according to TP/(TP+FN); specificity given in % according to TN/(TN+FP); positive predictive value given in % according to TP/(TP+FP); negative predictive value given in % according to TN/(TN+FN); accuracy given in % according to (TP+TN)/(TP+TN+FP+FN). Number true positive (TP); number true negative (TN); number false positive (FP); number false negative (FN).
4 to 6 months
Secondary Outcomes (5)
Perceived image quality
4 to 6 months
Image quality - noise
4 to 6 months
Image quality - contrast-to-noise ratio
4 to 6 months
Radiation dose
4 to 6 months
Diagnoses
4 to 6 months
Study Arms (1)
Abdominal Pain
Participants under evaluation for an acute abdominal condition who are referred to CT of the abdomen and pelvis.
Interventions
Low-dose CT scan will be performed, not exceeding 30% radiation dose of the standard CT. Low-dose CT images will be reconstructed with TrueFidelity high. The low-dose CT will be performed directly after the standard CT to avoid bias from differences in the timing of the contrast phase.
Eligibility Criteria
primary care clinic; university hospital
You may qualify if:
- Patients under evaluation for an acute abdominal condition who are referred to CT of the abdomen and pelvis.
- Age \>18 years
- The patients must be able to give their oral and written consent to study participation.
You may not qualify if:
- Contraindications regarding contrast enhanced CT examinations like known iodinated contrast media adverse reactions or claustrophobia.
- Pregnancy.
Contact the study team to confirm eligibility.
Sponsors & Collaborators
- Oslo University Hospitallead
- Odense University Hospitalcollaborator
Study Sites (2)
Odense University Hospital
Odense, Denmark
Oslo University Hospital
Oslo, Norway
Related Publications (29)
Brenner DJ, Hall EJ. Computed tomography--an increasing source of radiation exposure. N Engl J Med. 2007 Nov 29;357(22):2277-84. doi: 10.1056/NEJMra072149. No abstract available.
PMID: 18046031BACKGROUNDNovelline RA, Rhea JT, Rao PM, Stuk JL. Helical CT in emergency radiology. Radiology. 1999 Nov;213(2):321-39. doi: 10.1148/radiology.213.2.r99nv01321.
PMID: 10551209BACKGROUNDOECD. Computed tomography (CT) exams. 2018.
BACKGROUNDBerrington de Gonzalez A, Mahesh M, Kim KP, Bhargavan M, Lewis R, Mettler F, Land C. Projected cancer risks from computed tomographic scans performed in the United States in 2007. Arch Intern Med. 2009 Dec 14;169(22):2071-7. doi: 10.1001/archinternmed.2009.440.
PMID: 20008689BACKGROUNDMettler FA Jr, Thomadsen BR, Bhargavan M, Gilley DB, Gray JE, Lipoti JA, McCrohan J, Yoshizumi TT, Mahesh M. Medical radiation exposure in the U.S. in 2006: preliminary results. Health Phys. 2008 Nov;95(5):502-7. doi: 10.1097/01.HP.0000326333.42287.a2.
PMID: 18849682BACKGROUNDPan X, Sidky EY, Vannier M. Why do commercial CT scanners still employ traditional, filtered back-projection for image reconstruction? Inverse Probl. 2009 Jan 1;25(12):1230009. doi: 10.1088/0266-5611/25/12/123009.
PMID: 20376330BACKGROUNDBeister M, Kolditz D, Kalender WA. Iterative reconstruction methods in X-ray CT. Phys Med. 2012 Apr;28(2):94-108. doi: 10.1016/j.ejmp.2012.01.003. Epub 2012 Feb 10.
PMID: 22316498BACKGROUNDHsieh JL, E.; Nett, B.; Tang, J.; Thibault JB.; Sahney, S. A new era of image reconstruction: TrueFidelity. Technical white paper on deep learning image reconstruction. 2019.
BACKGROUNDAkagi M, Nakamura Y, Higaki T, Narita K, Honda Y, Zhou J, Yu Z, Akino N, Awai K. Deep learning reconstruction improves image quality of abdominal ultra-high-resolution CT. Eur Radiol. 2019 Nov;29(11):6163-6171. doi: 10.1007/s00330-019-06170-3. Epub 2019 Apr 11.
PMID: 30976831BACKGROUNDJensen CT, Liu X, Tamm EP, Chandler AG, Sun J, Morani AC, Javadi S, Wagner-Bartak NA. Image Quality Assessment of Abdominal CT by Use of New Deep Learning Image Reconstruction: Initial Experience. AJR Am J Roentgenol. 2020 Jul;215(1):50-57. doi: 10.2214/AJR.19.22332. Epub 2020 Apr 14.
PMID: 32286872BACKGROUNDNjolstad T, Schulz A, Godt JC, Brogger HM, Johansen CK, Andersen HK, Martinsen ACT. Improved image quality in abdominal computed tomography reconstructed with a novel Deep Learning Image Reconstruction technique - initial clinical experience. Acta Radiol Open. 2021 Apr 9;10(4):20584601211008391. doi: 10.1177/20584601211008391. eCollection 2021 Apr.
PMID: 33889427BACKGROUNDSolomon J, Lyu P, Marin D, Samei E. Noise and spatial resolution properties of a commercially available deep learning-based CT reconstruction algorithm. Med Phys. 2020 Sep;47(9):3961-3971. doi: 10.1002/mp.14319. Epub 2020 Jul 6.
PMID: 32506661BACKGROUNDGreffier J, Hamard A, Pereira F, Barrau C, Pasquier H, Beregi JP, Frandon J. Image quality and dose reduction opportunity of deep learning image reconstruction algorithm for CT: a phantom study. Eur Radiol. 2020 Jul;30(7):3951-3959. doi: 10.1007/s00330-020-06724-w. Epub 2020 Feb 25.
PMID: 32100091BACKGROUNDBrady SL, Trout AT, Somasundaram E, Anton CG, Li Y, Dillman JR. Improving Image Quality and Reducing Radiation Dose for Pediatric CT by Using Deep Learning Reconstruction. Radiology. 2021 Jan;298(1):180-188. doi: 10.1148/radiol.2020202317. Epub 2020 Nov 17.
PMID: 33201790BACKGROUNDLarson DB, Johnson LW, Schnell BM, Salisbury SR, Forman HP. National trends in CT use in the emergency department: 1995-2007. Radiology. 2011 Jan;258(1):164-73. doi: 10.1148/radiol.10100640. Epub 2010 Nov 29.
PMID: 21115875BACKGROUNDExpert Panel on Gastrointestinal Imaging:; Garcia EM, Camacho MA, Karolyi DR, Kim DH, Cash BD, Chang KJ, Feig BW, Fowler KJ, Kambadakone AR, Lambert DL, Levy AD, Marin D, Moreno C, Peterson CM, Scheirey CD, Siegel A, Smith MP, Weinstein S, Carucci LR. ACR Appropriateness Criteria(R) Right Lower Quadrant Pain-Suspected Appendicitis. J Am Coll Radiol. 2018 Nov;15(11S):S373-S387. doi: 10.1016/j.jacr.2018.09.033.
PMID: 30392606BACKGROUNDExpert Panel on Gastrointestinal Imaging:; Peterson CM, McNamara MM, Kamel IR, Al-Refaie WB, Arif-Tiwari H, Cash BD, Chernyak V, Goldstein A, Grajo JR, Hindman NM, Horowitz JM, Noto RB, Porter KK, Srivastava PK, Zaheer A, Carucci LR. ACR Appropriateness Criteria(R) Right Upper Quadrant Pain. J Am Coll Radiol. 2019 May;16(5S):S235-S243. doi: 10.1016/j.jacr.2019.02.013.
PMID: 31054750BACKGROUNDRud B, Vejborg TS, Rappeport ED, Reitsma JB, Wille-Jorgensen P. Computed tomography for diagnosis of acute appendicitis in adults. Cochrane Database Syst Rev. 2019 Nov 19;2019(11):CD009977. doi: 10.1002/14651858.CD009977.pub2.
PMID: 31743429BACKGROUNDKabir SA, Kabir SI, Sun R, Jafferbhoy S, Karim A. How to diagnose an acutely inflamed appendix; a systematic review of the latest evidence. Int J Surg. 2017 Apr;40:155-162. doi: 10.1016/j.ijsu.2017.03.013. Epub 2017 Mar 6.
PMID: 28279749BACKGROUNDMoloney F, James K, Twomey M, Ryan D, Grey TM, Downes A, Kavanagh RG, Moore N, Murphy MJ, Bye J, Carey BW, McSweeney SE, Deasy C, Andrews E, Shanahan F, Maher MM, O'Connor OJ. Low-dose CT imaging of the acute abdomen using model-based iterative reconstruction: a prospective study. Emerg Radiol. 2019 Apr;26(2):169-177. doi: 10.1007/s10140-018-1658-z. Epub 2018 Nov 17.
PMID: 30448900BACKGROUNDPoletti PA, Becker M, Becker CD, Halfon Poletti A, Rutschmann OT, Zaidi H, Perneger T, Platon A. Emergency assessment of patients with acute abdominal pain using low-dose CT with iterative reconstruction: a comparative study. Eur Radiol. 2017 Aug;27(8):3300-3309. doi: 10.1007/s00330-016-4712-9. Epub 2017 Jan 12.
PMID: 28083698BACKGROUNDWidmark A. Diagnostic reference level (DRL) in Norway 2017. Results, revision:and establishment of new DRL.NRPA Report 2018:3. Norwegian Radiation Protection Authority, Østerås 2018.
BACKGROUNDKomperød M, Rudjord AL, Skuterud L, Dyve JE. Radiation Doses from the Environment. Calculations of the Public's Exposure to Radiation from the Environment in Norway. Strålevern Rapport 2015:11 Østerås: Norwegian Radiation Protection Authority 2015.
BACKGROUNDBossuyt PM, Reitsma JB, Bruns DE, Gatsonis CA, Glasziou PP, Irwig L, Lijmer JG, Moher D, Rennie D, de Vet HC, Kressel HY, Rifai N, Golub RM, Altman DG, Hooft L, Korevaar DA, Cohen JF; STARD Group. STARD 2015: An Updated List of Essential Items for Reporting Diagnostic Accuracy Studies. Radiology. 2015 Dec;277(3):826-32. doi: 10.1148/radiol.2015151516. Epub 2015 Oct 28.
PMID: 26509226BACKGROUNDSounderajah V, Ashrafian H, Golub RM, Shetty S, De Fauw J, Hooft L, Moons K, Collins G, Moher D, Bossuyt PM, Darzi A, Karthikesalingam A, Denniston AK, Mateen BA, Ting D, Treanor D, King D, Greaves F, Godwin J, Pearson-Stuttard J, Harling L, McInnes M, Rifai N, Tomasev N, Normahani P, Whiting P, Aggarwal R, Vollmer S, Markar SR, Panch T, Liu X; STARD-AI Steering Committee. Developing a reporting guideline for artificial intelligence-centred diagnostic test accuracy studies: the STARD-AI protocol. BMJ Open. 2021 Jun 28;11(6):e047709. doi: 10.1136/bmjopen-2020-047709.
PMID: 34183345BACKGROUNDMongan J, Moy L, Kahn CE Jr. Checklist for Artificial Intelligence in Medical Imaging (CLAIM): A Guide for Authors and Reviewers. Radiol Artif Intell. 2020 Mar 25;2(2):e200029. doi: 10.1148/ryai.2020200029. eCollection 2020 Mar. No abstract available.
PMID: 33937821BACKGROUNDReport EUR 16262 EN. European guidelines on quality criteria for computed tomography. 2000.
BACKGROUNDAhn S, Park SH, Lee KH. How to demonstrate similarity by using noninferiority and equivalence statistical testing in radiology research. Radiology. 2013 May;267(2):328-38. doi: 10.1148/radiol.12120725.
PMID: 23610094BACKGROUNDEng KA, Abadeh A, Ligocki C, Lee YK, Moineddin R, Adams-Webber T, Schuh S, Doria AS. Acute Appendicitis: A Meta-Analysis of the Diagnostic Accuracy of US, CT, and MRI as Second-Line Imaging Tests after an Initial US. Radiology. 2018 Sep;288(3):717-727. doi: 10.1148/radiol.2018180318. Epub 2018 Jun 19.
PMID: 29916776BACKGROUND
MeSH Terms
Conditions
Condition Hierarchy (Ancestors)
Study Officials
- PRINCIPAL INVESTIGATOR
Anselm Schulz, PhD
Oslo University Hospital
Study Design
- Study Type
- observational
- Observational Model
- COHORT
- Time Perspective
- PROSPECTIVE
- Sponsor Type
- OTHER
- Responsible Party
- PRINCIPAL INVESTIGATOR
- PI Title
- MD, PhD
Study Record Dates
First Submitted
November 15, 2022
First Posted
December 15, 2022
Study Start
December 7, 2022
Primary Completion
July 10, 2023
Study Completion
July 10, 2023
Last Updated
October 17, 2023
Record last verified: 2023-10
Data Sharing
- IPD Sharing
- Will not share
IPD will not be shared due to legal and privacy Issues.