Xiaotong Yang1,2, Wenge Sun1, Desheng Huang3, Hui Li4, Yu Zhao1, Peiling Li1, Yi Liu1. 1. Department of Radiology, The First Hospital of China Medical University, Shenyang, China. 2. Department of Radiology, Liaocheng People's Hospital, Liaocheng, China. 3. Department of Mathematics, School of Fundamental Sciences, China Medical University, Shenyang, China. 4. Department of Radiology, Shengjing Hospital of China Medical University, Shenyang, China.
Abstract
BACKGROUND: This study aimed to use the stretched-exponential nonlinear regression analysis model to explore the value of the energy spectral curve in the differential diagnosis of clear cell renal cell carcinoma (ccRCC), minimal fat renal angiomyolipoma (RAML), and hypovascular renal cell carcinoma. METHODS: Sixty-five cases with renal tumors were enrolled retrospectively who had undergone a preoperative multiphase spectral CT scan of the kidney in pre-enhance and double-phase enhanced scanning. The normalized iodine concentrations (NIC) of these lesions, normal renal cortex, and psoas major were measured and calculated. The spectral curves of these lesions and normal tissues were analyzed to calculate the stretched-exponential index (α) and b value with the stretched-exponential nonlinear regression analysis model (y=-b·Xα). The differences between α, b value, and NIC of these lesions and normal tissues in pre-enhance and two enhanced phases were compared using one-way ANOVA. The correlation between α, b value, and NIC was evaluated using the Pearson coefficient test, with significance assigned at the 5% level. RESULTS: There was no significant difference in α value between the groups in pre-enhance scanning. In the Cortical phase (CP), there were no significant differences in NIC and α value between minimal fat RAML and hypovascular renal cell carcinoma, or between ccRCC and the normal renal cortex. However, in the nephrographic phase (NP), a significant difference in α value was found between minimal fat RAML and hypovascular renal cell carcinoma, but no difference in NIC between them. In NP, there were significant differences in NIC and α values between ccRCC and the normal renal cortex. In CP and NP, there were significant differences between the psoas major and other groups in all parameters. For b value, in pre-enhance scanning, there was a significant difference between the psoas major and other groups, and between ccRCC and the normal renal cortex. There was no significant difference between other groups. After enhancement, in CP and NP, significant differences were observed between the psoas major and other groups in b value, but no significant differences were observed between all renal tumors and the normal renal cortex. A linear correlation was found between α values and NIC in CP (r=0.780, P=0.00) and NP (r=0.693, P=0.00). The b values and NIC had a low correlation in CP, with no correlation in NP. CONCLUSIONS: Quantitative spectral CT with the stretched-exponential nonlinear regression analysis model may enhance the differential diagnosis ability for renal tumors. Its clinical value remains to be further explored in other types of soft tissue lesions. 2021 Quantitative Imaging in Medicine and Surgery. All rights reserved.
BACKGROUND: This study aimed to use the stretched-exponential nonlinear regression analysis model to explore the value of the energy spectral curve in the differential diagnosis of clear cell renal cell carcinoma (ccRCC), minimal fat renal angiomyolipoma (RAML), and hypovascular renal cell carcinoma. METHODS: Sixty-five cases with renal tumors were enrolled retrospectively who had undergone a preoperative multiphase spectral CT scan of the kidney in pre-enhance and double-phase enhanced scanning. The normalized iodine concentrations (NIC) of these lesions, normal renal cortex, and psoas major were measured and calculated. The spectral curves of these lesions and normal tissues were analyzed to calculate the stretched-exponential index (α) and b value with the stretched-exponential nonlinear regression analysis model (y=-b·Xα). The differences between α, b value, and NIC of these lesions and normal tissues in pre-enhance and two enhanced phases were compared using one-way ANOVA. The correlation between α, b value, and NIC was evaluated using the Pearson coefficient test, with significance assigned at the 5% level. RESULTS: There was no significant difference in α value between the groups in pre-enhance scanning. In the Cortical phase (CP), there were no significant differences in NIC and α value between minimal fat RAML and hypovascular renal cell carcinoma, or between ccRCC and the normal renal cortex. However, in the nephrographic phase (NP), a significant difference in α value was found between minimal fat RAML and hypovascular renal cell carcinoma, but no difference in NIC between them. In NP, there were significant differences in NIC and α values between ccRCC and the normal renal cortex. In CP and NP, there were significant differences between the psoas major and other groups in all parameters. For b value, in pre-enhance scanning, there was a significant difference between the psoas major and other groups, and between ccRCC and the normal renal cortex. There was no significant difference between other groups. After enhancement, in CP and NP, significant differences were observed between the psoas major and other groups in b value, but no significant differences were observed between all renal tumors and the normal renal cortex. A linear correlation was found between α values and NIC in CP (r=0.780, P=0.00) and NP (r=0.693, P=0.00). The b values and NIC had a low correlation in CP, with no correlation in NP. CONCLUSIONS: Quantitative spectral CT with the stretched-exponential nonlinear regression analysis model may enhance the differential diagnosis ability for renal tumors. Its clinical value remains to be further explored in other types of soft tissue lesions. 2021 Quantitative Imaging in Medicine and Surgery. All rights reserved.
Entities:
Keywords:
Spectral curve; renal tumor; stretched-exponential analysis model
Authors: Ashok Srinivasan; Ellen Hoeffner; Mohannad Ibrahim; Gaurang V Shah; Frank LaMarca; Suresh K Mukherji Journal: AJR Am J Roentgenol Date: 2013-10 Impact factor: 3.959