Computed tomography based cross-sectional anatomy of the pelvis predicts surgical outcome after rectal cancer surgery
10.4174/astr.2020.99.2.90
- Author:
Gyoung Tae NOH
1
;
Soon Sup CHUNG
;
Kwang Ho KIM
;
Ryung-Ah LEE
Author Information
1. Department of Surgery, Ewha Womans University College of Medicine, Seoul, Korea
- Publication Type:ORIGINAL ARTICLE
- From:Annals of Surgical Treatment and Research
2020;99(2):90-96
- CountryRepublic of Korea
-
Abstract:
Purpose:Narrow pelvis has been considered an adverse factor for postoperative and oncologic outcomes after rectal cancer surgery. The aim of this study was to investigate the validity of using only axial CT scan images to calculate the pelvic cross-sectional area for the prediction of adverse outcomes after rectal cancer surgery.
Methods:The medical records of patients who underwent rectal cancer surgery were reviewed and analyzed retrospectively. Axial images of CT scan were used to measure the pelvic cross-sectional area. Pelvic surgical site infection (SSI), positive resection margin, and early local recurrence were adopted as end-points to analyze the impact of pelvimetry on surgical outcome.
Results:The mean pelvic cross-sectional area was 84.3 ± 10.9 cm2. Males had significantly smaller pelvic areas than females (P < 0.001). Comparing pelvic cross-sectional areas according to the surgical outcomes, the results indicated that patients with pelvic SSI and local failure (positive resection margin or local recurrence within 1 year) have significantly smaller cross-sectional-area than SSI and local failure-free patients (P = 0.013 and P = 0.031). A calculated crosssectional area of 88.8 cm2 was determined as the cutoff value for the prediction of pelvic SSI and/or local failure, which was significant in a validating analysis.
Conclusion:The pelvic cross-sectional area obtained from a routine axial CT scan image was associated with pelvic SSI, positive resection margin, and early local recurrence. It might be an intuitive, feasible, and easily adoptable method for predicting surgical outcomes.
