ABSTRACT
Objective:
To validate the predictive
power of newly developed
magnetic resonance (MR) morphological and clinicopathological
risk models in predicting
low anterior resection syndrome (LARS) 6 months after anterior resection of middle and low
rectal cancer (MLRC).
Methods:
From May 2018 to January 2021, 236
patients with MLRC admitted to two
hospitals (internal and external validation) were included. MR images, clinicopathological data, and LARS scores (LARSS) were collected.
Tumor morphology data included longitudinal involvement length, maximum
tumor diameter, proportion of
tumor to circumference of the intestinal wall,
tumor mesorectal infiltration depth, circumferential margin status, and distance between the
tumor and anal margins. Pelvic measurements included anorectal angle, mesenterial volume (MRV), and pelvic volume. Univariate and multivariate
logistic regression was used to obtain independent
risk factors of LARS after anterior resection Then, the prediction model was constructed, expressed as a
nomogram, and its internal and external validity was assessed using
receiver operating characteristic curves.
Results:
The uni- and
multivariate analysis revealed distance between the
tumor and anal margins, MRV, pelvic volume, and
body weight as significant independent
risk factors for predicting LARS. From the
nomogram, the area under the curve (
AUC),
sensitivity, and
specificity were 0.835, 75.0 %, and 80.4 %, respectively. The
AUC,
sensitivity, and
specificity in the external validation group were 0.874, 83.3 %, and 91.7 %, respectively.
Conclusion:
This study shows that MR imaging and clinicopathology presented by a
nomogram can strongly predict LARSS, which can then individually predict LARS 6 months after anterior resection in
patients with MLRC and facilitate
clinical decision-making.
Clinical relevance statement We believe that our study makes a significant contribution to the
literature. This
method of predicting postoperative anorectal function by preoperative measurement of MRV provides a new tool for clinicians to study LARS.
