Original Article
Establishment of a novel system for the preoperative prediction of adherent perinephric fat (APF) occurrence based on a multi-mode and multi-parameter analysis of dual-energy CT
Abstract
Background: Adherent perinephric fat (APF) is evaluated preoperatively with the Mayo adhesive probability (MAP) scoring system using conventional single-form computed tomography (CT) images. An objective or quantitative indicator for predicting APF is urgently needed for clinical application.
Methods: A total of 150 patients with renal tumours who underwent laparoscopic partial nephrectomy (LPN) were retrospectively enrolled and divided into the APF group (n=100) and the non-APF group (n=50) according to surgical results. All patients underwent a renal contrast-enhanced dual-energy CT (DECT) scan. The obtained CT DICOM data were transmitted to the DECT post-processing workstation and adopted virtual non-contrast (VNC), Rho/Z Maps, and Monoenergetic Plus (mono+) modes separately to undergo a multi-parameter analysis. A logistic stepwise investigation was utilized to analyse the related risk factors. The cutoff value was determined by the Youden index. Fifty patients were prospectively enrolled to validate the constructed model. The area under the curve (AUC), sensitivity, specificity and accuracy of the model were calculated.
Results: The study demonstrated that age, sex, body mass index (BMI), smoking status, tumour diameter, exophytic status, degree of malignancy and posterior perinephric fat thickness were related to the occurrence of APF (P<0.05). Model 1 was selected with the contrast material (CM) parameter (cutoff point 0.5), model 2 was selected with the effective atomic number (Zeff) parameter (cutoff point 6.5), and model 3 was selected with the slope K (K) parameter (cutoff point −0.95). The AUC, sensitivity, specificity and accuracy of model 1 were 0.94, 0.94, 0.93 and 0.94, respectively; for model 2, they were 0.94, 0.93, 0.93 and 0.96, respectively; and for model 3, they were 0.92, 0.92, 0.93 and 0.92, respectively.
Conclusions: Multi-mode and multi-parameter models of DECT can effectively be used to predict the occurrence of APF.
Methods: A total of 150 patients with renal tumours who underwent laparoscopic partial nephrectomy (LPN) were retrospectively enrolled and divided into the APF group (n=100) and the non-APF group (n=50) according to surgical results. All patients underwent a renal contrast-enhanced dual-energy CT (DECT) scan. The obtained CT DICOM data were transmitted to the DECT post-processing workstation and adopted virtual non-contrast (VNC), Rho/Z Maps, and Monoenergetic Plus (mono+) modes separately to undergo a multi-parameter analysis. A logistic stepwise investigation was utilized to analyse the related risk factors. The cutoff value was determined by the Youden index. Fifty patients were prospectively enrolled to validate the constructed model. The area under the curve (AUC), sensitivity, specificity and accuracy of the model were calculated.
Results: The study demonstrated that age, sex, body mass index (BMI), smoking status, tumour diameter, exophytic status, degree of malignancy and posterior perinephric fat thickness were related to the occurrence of APF (P<0.05). Model 1 was selected with the contrast material (CM) parameter (cutoff point 0.5), model 2 was selected with the effective atomic number (Zeff) parameter (cutoff point 6.5), and model 3 was selected with the slope K (K) parameter (cutoff point −0.95). The AUC, sensitivity, specificity and accuracy of model 1 were 0.94, 0.94, 0.93 and 0.94, respectively; for model 2, they were 0.94, 0.93, 0.93 and 0.96, respectively; and for model 3, they were 0.92, 0.92, 0.93 and 0.92, respectively.
Conclusions: Multi-mode and multi-parameter models of DECT can effectively be used to predict the occurrence of APF.
