alexa Creation of an Accurate Artificial Neural Network Prediction Model of Radiologist Reported CT Features for Colorectal Anastomotic Leaks | OMICS International| Abstract
ISSN:2167-7964

OMICS Journal of Radiology
Open Access

OMICS International organises 3000+ Global Conferenceseries Events every year across USA, Europe & Asia with support from 1000 more scientific Societies and Publishes 700+ Open Access Journals which contains over 50000 eminent personalities, reputed scientists as editorial board members.

Open Access Journals gaining more Readers and Citations
700 Journals and 15,000,000 Readers Each Journal is getting 25,000+ Readers

This Readership is 10 times more when compared to other Subscription Journals (Source: Google Analytics)
All submissions of the EM system will be redirected to Online Manuscript Submission System. Authors are requested to submit articles directly to Online Manuscript Submission System of respective journal.
  • Research Article   
  • OMICS J Radiol 2019, Vol 8(2): 307
  • DOI: 10.4172/2167-7964.1000307

Creation of an Accurate Artificial Neural Network Prediction Model of Radiologist Reported CT Features for Colorectal Anastomotic Leaks

Adams K1*, Hansmann A2, Bosanac D2, Peddu P2, Ryan S2 and Papagrigoriadis S1
1Department of Colorectal Surgery, King’s College Hospital, London, UK
2Department of Radiology, King’s College Hospital, London, UK
*Corresponding Author : Adams K, Department of Colorectal Surgery, King’s College Hospital, London, UK, Tel: +61481240128, Email: [email protected]

Received Date: Feb 25, 2019 / Accepted Date: Mar 24, 2019 / Published Date: Mar 31, 2019

Abstract

Objective: As colorectal anastomotic leaks (AL) often present with non-specific clinical features, Computed Tomography (CT) scans are commonly used to aid in diagnosis. Aim was to define radiologist reported features in CT scans following colorectal resection as diagnostic factors for clinical AL detection.

Methods: Consecutive patients identified with a clinically confirmed post-operative AL. Control group (matched 2:1 ratio) selected from patients who were scanned with a clinical suspicion of an AL, though eventually disproved and who did not require re-operation. Four gastrointestinal radiologists reviewed CT scans, blinded to clinical outcome. Radiologists assessed for the overall impression of a radiological AL and presence of the adjunct leak features. A leak prediction model was constructed with multivariate logistic regression with outcome classified as clinical AL.

Results: 18 patients with confirmed AL, 36 matched control patients. No significant difference in the sensitivity/specificity between the radiologists in accuracy of leak detection, with overall correct diagnosis of clinical AL 81.4%. Radiological Leak, abnormal bowel wall appearance and ileus were significant predictors (P<0.05) within regression model. The prediction model produced an overall sensitivity 85.2%, specificity 80.2% and ROC curve area of 87.3%.

Conclusion: Individual radiologist reported CT features have been used to create a risk prediction model that improves diagnostic accuracy of AL over general radiological impression alone.

Keywords: Anastomotic leak; Colorectal; Artificial neural network; Imaging; Computed tomography

Citation: Adams K, Hansmann A, Bosanac D, Peddu P, Ryan S, et al. (2019) Creation of an Accurate Artificial Neural Network Prediction Model of Radiologist Reported CT Features for Colorectal Anastomotic Leaks. OMICS J Radiol 8:307. Doi: 10.4172/2167-7964.1000307

Copyright: © 2019 Adams K, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Select your language of interest to view the total content in your interested language

Post Your Comment Citation
Share This Article
Article Usage
  • Total views: 533
  • [From(publication date): 0-0 - Nov 12, 2019]
  • Breakdown by view type
  • HTML page views: 489
  • PDF downloads: 44
Top