Original Article
Application value of dual-energy computed tomography spectrum curve combined with clinical risk factors in predicting adherent perinephric fat
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: Data obtained from 150 renal tumor patients with partial nephrectomy (PN) were used in the present study. Patients were divided into two groups based on the surgical results: an APF group (n=100) and a non-APF group (n=50). All patients had renal contrast-enhanced dual-energy CT (DECT) scan, and the data were brought into a post-processing workstation. Monoenergetic plus mode was adopted to analyze the spectrum curve of the region of interest (ROI). Logistic stepwise relapse investigation was utilized to analyze the related risk factors. The maximum Youden index was taken as the cut-off value. The sensitivity, specificity, accuracy, and 95% CI of the model were calculated.
Results: APF is related to patients’ clinical characteristics of age, gender, BMI, smoking, tumor diameter, exophytic, and benign or malignant nature of the renal masses (P<0.05). The shape of the curve of ROI1 in the APF group was different from that of the ROI4 in the non-APF group, and the curve slope of K1 (−0.21±0.47) was different from that of K4 (−1.30±0.14) (P<0.001). Statistical analysis showed that the slope K was solely retained in the model index. The best cut-off point for the K value was −0.95. The AUC of the cut-off point was 0.97 (95% CI: 0.96−0.99).
Conclusions: The DECT spectrum curve performed well in predicting APF, and the curve slope K can be used as an objective quantitative indicator.
Methods: Data obtained from 150 renal tumor patients with partial nephrectomy (PN) were used in the present study. Patients were divided into two groups based on the surgical results: an APF group (n=100) and a non-APF group (n=50). All patients had renal contrast-enhanced dual-energy CT (DECT) scan, and the data were brought into a post-processing workstation. Monoenergetic plus mode was adopted to analyze the spectrum curve of the region of interest (ROI). Logistic stepwise relapse investigation was utilized to analyze the related risk factors. The maximum Youden index was taken as the cut-off value. The sensitivity, specificity, accuracy, and 95% CI of the model were calculated.
Results: APF is related to patients’ clinical characteristics of age, gender, BMI, smoking, tumor diameter, exophytic, and benign or malignant nature of the renal masses (P<0.05). The shape of the curve of ROI1 in the APF group was different from that of the ROI4 in the non-APF group, and the curve slope of K1 (−0.21±0.47) was different from that of K4 (−1.30±0.14) (P<0.001). Statistical analysis showed that the slope K was solely retained in the model index. The best cut-off point for the K value was −0.95. The AUC of the cut-off point was 0.97 (95% CI: 0.96−0.99).
Conclusions: The DECT spectrum curve performed well in predicting APF, and the curve slope K can be used as an objective quantitative indicator.
