Computed Tomography Image Analysis in Abdominal Wall Reconstruction: A Systematic Review. Issue 12 (December 2020)

• Record Type: Journal Article
• Authors: Elfanagely, Omar; Mellia, Joseph A.; Othman, Sammy; Basta, Marten N.; Mauch, Jaclyn T.; Fischer, John P.
• Is Part Of: Plastic and reconstructive surgery. Volume 8:Issue 12(2020)
• Publication Date: 2020-12
• Subjects: Surgery, Plastic -- Periodicals; Surgery, Plastic -- Periodicals; Reconstructive Surgical Procedures -- Periodicals; 617.95205
• DOI: 10.1097/GOX.0000000000003307
• Languages: English
• ISSNs: 2169-7574

Abstract

Abstract : Background: Ventral hernias are a complex and costly burden to the health care system. Although preoperative radiologic imaging is commonly performed, the plethora of anatomic features present and available in routine imaging are seldomly quantified and integrated into patient selection, preoperative risk stratification, and perioperative planning. We herein aimed to critically examine the current state of computed tomography feature application in predicting surgical outcomes. Methods: A systematic review was conducted in accordance with the Preferred Reporting Items for a Systematic Review and Meta-Analysis (PRISMA) checklist. PubMed, MEDLINE, and Embase databases were reviewed under search syntax "computed tomography imaging" and "abdominal hernia" for papers published between 2000 and 2020. Results: Of the initial 1922 studies, 12 papers met inclusion and exclusion criteria. The most frequently used radiologic features were hernia volume (n = 9), subcutaneous fat volume (n = 5), and defect size (n = 8). Outcomes included both complications and need for surgical intervention. Median area under the curve (AUC) and odds ratio were 0.68 (±0.16) and 1.12 (±0.39), respectively. The best predictive feature was hernia neck ratio > 2.5 (AUC 0.903). Conclusions: Computed tomography feature selection offers hernia surgeons an opportunity to identify, quantify, and integrate routinely available morphologic tissue features into preoperative decision-making. Despite being in its early stages, future surgeons and researchers will soon be able to integrate 3D volumetric analysis and complex machine learning and neural network models to improvement patient care.