BACKGROUND: Analytic morphometrics provides objective data that may better stratify risk. We investigated morphometrics and outcome among colon cancer patients. METHODS: An IRB-approved review identified 302 patients undergoing colectomy who had CT scans. These were processed to measure psoas area (PA), density (PD), subcutaneous fat (SFD), visceral fat (VF), and total body fat (TBF). Correlation with complications, recurrence, and survival were obtained by t-tests and linear regression models after adjusting for age and Charlson index. RESULTS: The best predictor of surgical complications was PD. PMH, Charlson, BMI, and age were not significant when PD was considered. SF area was the single best predictor of a wound infection. While all measures of obesity correlated with outcome, TBF was most predictive. Final multivariate Cox models for survival included age, Charlson score, nodal positivity, and TBF. CONCLUSIONS: Analytic morphometric analysis provided objective data that stratified complications and outcome better than age, BMI, or co-morbidities.
BACKGROUND: Analytic morphometrics provides objective data that may better stratify risk. We investigated morphometrics and outcome among colon cancerpatients. METHODS: An IRB-approved review identified 302 patients undergoing colectomy who had CT scans. These were processed to measure psoas area (PA), density (PD), subcutaneous fat (SFD), visceral fat (VF), and total body fat (TBF). Correlation with complications, recurrence, and survival were obtained by t-tests and linear regression models after adjusting for age and Charlson index. RESULTS: The best predictor of surgical complications was PD. PMH, Charlson, BMI, and age were not significant when PD was considered. SF area was the single best predictor of a wound infection. While all measures of obesity correlated with outcome, TBF was most predictive. Final multivariate Cox models for survival included age, Charlson score, nodal positivity, and TBF. CONCLUSIONS: Analytic morphometric analysis provided objective data that stratified complications and outcome better than age, BMI, or co-morbidities.
Authors: Jeffery Chakedis; Gaya Spolverato; Eliza W Beal; Ingrid Woelfel; Fabio Bagante; Katiuscha Merath; Steven H Sun; Aaron Chafitz; Jason Galo; Mary Dillhoff; Jordan Cloyd; Timothy M Pawlik Journal: J Gastrointest Surg Date: 2018-05-31 Impact factor: 3.452
Authors: Candyce H Kroenke; Carla M Prado; Jeffrey A Meyerhardt; Erin K Weltzien; Jingjie Xiao; Elizabeth M Cespedes Feliciano; Bette J Caan Journal: Cancer Date: 2018-05-24 Impact factor: 6.860
Authors: Andrew J Benjamin; Mary M Buschmann; Andrew Schneider; Brian A Derstine; Jeffrey F Friedman; Stewart C Wang; William Dale; Kevin K Roggin Journal: J Gastrointest Surg Date: 2017-03-24 Impact factor: 3.452
Authors: Y Zhang; J P Wang; X L Wang; H Tian; T T Gao; L M Tang; F Tian; J W Wang; H J Zheng; L Zhang; X J Gao; G L Li; X Y Wang Journal: Curr Oncol Date: 2018-10-31 Impact factor: 3.677
Authors: B C Boer; F de Graaff; M Brusse-Keizer; D E Bouman; C H Slump; M Slee-Valentijn; J M Klaase Journal: Int J Colorectal Dis Date: 2016-02-15 Impact factor: 2.571
Authors: Ching-Di Chang; Jim S Wu; Jennifer Ni Mhuircheartaigh; Marry G Hochman; Edward K Rodriguez; Paul T Appleton; Colm J Mcmahon Journal: Skeletal Radiol Date: 2017-12-15 Impact factor: 2.199