Creation of an Accurate Artificial Neural Network Prediction Model of Radiologist Reported CT Features for Colorectal Anastomotic Leaks
Received Date: Feb 25, 2019 / Accepted Date: Mar 24, 2019 / Published Date: Mar 31, 2019
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.
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