Quantitative CT Imaging of Ventral Hernias: Preliminary Validation of an Anatomical Labeling Protocol

Objective We described and validated a quantitative anatomical labeling protocol for extracting clinically relevant quantitative parameters for ventral hernias (VH) from routine computed tomography (CT) scans. This information was then used to predict the need for mesh bridge closure during ventral hernia repair (VHR). Methods A detailed anatomical labeling protocol was proposed to enable quantitative description of VH including shape, location, and surrounding environment (61 scans). Intra- and inter-rater reproducibilities were calculated for labeling on 18 and 10 clinically acquired CT scans, respectively. Preliminary clinical validation was performed by correlating 20 quantitative parameters derived from anatomical labeling with the requirement for mesh bridge closure at surgery (26 scans). Prediction of this clinical endpoint was compared with similar models fit on metrics from the semi-quantitative European Hernia Society Classification for Ventral Hernia (EHSCVH). Results High labeling reproducibilities were achieved for abdominal walls (±2 mm in mean surface distance), key anatomical landmarks (±5 mm in point distance), and hernia volumes (0.8 in Cohen’s kappa). 9 out of 20 individual quantitative parameters of hernia properties were significantly different between patients who required mesh bridge closure versus those in whom fascial closure was achieved at the time of VHR (p<0.05). Regression models constructed by two to five metrics presented a prediction with 84.6% accuracy for bridge requirement with cross-validation; similar models constructed by EHSCVH variables yielded 76.9% accuracy. Significance Reproducibility was acceptable for this first formal presentation of a quantitative image labeling protocol for VH on abdominal CT. Labeling-derived metrics presented better prediction of the need for mesh bridge closure than the EHSCVH metrics. This effort is intended as the foundation for future outcomes studies attempting to optimize choice of surgical technique across different anatomical types of VH.

[1]  H. Goor,et al.  Criteria for definition of a complex abdominal wall hernia , 2014, Hernia.

[2]  Matthew J. McAuliffe,et al.  Medical Image Processing, Analysis and Visualization in clinical research , 2001, Proceedings 14th IEEE Symposium on Computer-Based Medical Systems. CBMS 2001.

[3]  M. Rosen,et al.  Modified hernia grading scale to stratify surgical site occurrence after open ventral hernia repairs. , 2012, Journal of the American College of Surgeons.

[4]  J. Regimbeau,et al.  Peritoneal volume is predictive of tension-free fascia closure of large incisional hernias with loss of domain: a prospective study , 2011, Hernia.

[5]  M. Urbanchek,et al.  Incisional Herniation Induces Decreased Abdominal Wall Compliance via Oblique Muscle Atrophy and Fibrosis , 2007, Annals of surgery.

[6]  Rodney X. Sturdivant,et al.  Introduction to the Logistic Regression Model , 2005 .

[7]  M. Rosen,et al.  Incisional ventral hernias: review of the literature and recommendations regarding the grading and technique of repair. , 2010, Surgery.

[8]  Sheng Yao,et al.  Significance of measurements of herniary area and volume and abdominal cavity volume in the treatment of incisional hernia: Application of CT 3D reconstruction in 17 cases , 2012, Computer aided surgery : official journal of the International Society for Computer Aided Surgery.

[9]  採編典藏組 Society for Industrial and Applied Mathematics(SIAM) , 2008 .

[10]  Ronald M. Summers,et al.  Fully automated adipose tissue measurement on abdominal CT , 2011, Medical Imaging.

[11]  S. Phillips,et al.  Epidemiology and cost of ventral hernia repair: making the case for hernia research , 2012, Hernia.

[12]  Nello Cristianini,et al.  An Introduction to Support Vector Machines and Other Kernel-based Learning Methods , 2000 .

[13]  Jesse M. Ehrenfeld,et al.  History of MRSA Infection Considerably Increases Risk of Surgical Site Infection in Ventral Hernia Repair , 2014 .

[14]  H. Zou,et al.  Regularization and variable selection via the elastic net , 2005 .

[15]  R. Baucom,et al.  Texture analysis improves level set segmentation of the anterior abdominal wall. , 2013, Medical physics.

[16]  L. Bencini,et al.  Incisional hernia repair , 2003, Surgical Endoscopy And Other Interventional Techniques.

[17]  J. R. Landis,et al.  The measurement of observer agreement for categorical data. , 1977, Biometrics.

[18]  R. McLeod,et al.  Meta‐analysis of randomized controlled trials comparing open and laparoscopic ventral and incisional hernia repair with mesh , 2009, The British journal of surgery.

[19]  E. Utiyama,et al.  A computerized tomography scan method for calculating the hernia sac and abdominal cavity volume in complex large incisional hernia with loss of domain , 2010, Hernia.

[20]  J. Jeekel,et al.  A comparison of suture repair with mesh repair for incisional hernia. , 2000, The New England journal of medicine.

[21]  G. Campanelli,et al.  Classification of primary and incisional abdominal wall hernias , 2009, Hernia.

[22]  G. Wahba Spline models for observational data , 1990 .