Intravoxel Incoherent Motion Model in Differentiating the Pathological Grades of Esophageal Carcinoma: Comparison of Mono-Exponential and Bi-Exponential Fit Model

Abstract

PurposeTo compare the diagnostic efficiency of the mono-exponential model and bi-exponential model deriving from intravoxel incoherent motion diffusion-weighted imaging (IVIM-DWI) in differentiating the pathological grade of esophageal squamous cell carcinoma (ESCC).MethodsFifty-four patients with ESCC were divided into three groups of poorly-differentiated (PD), moderately-differentiated (MD), and well-differentiated (WD), and underwent the IVIM-DWI scan. Mono-exponential (Dmono, D*mono, and fmono) and bi-exponential fit parameters (Dbi, D*bi, and fbi) were calculated using the IVIM data for the tumors. Mean parameter values of three groups were compared using a one-way ANOVA followed by post hoc tests. The receiver operating characteristic curve was drawn for differentiating pathological grade of ESCC. Correlations between pathological grades and IVIM parameters were analyzed.ResultsThere were significant differences in fmono and fbi among the PD, MD and WD ESCC groups (all p<0.05). The fmono were 0.32 ± 0.07, 0.23 ± 0.08, and 0.16 ± 0.05, respectively, and the fbi were 0.35 ± 0.08, 0.26 ± 0.10, and 0.18 ± 0.07, respectively. There was a significant difference in the Dmono between the WD and the PD group (1.48 ± 0.51* 10-3 mm2/s versus 1.05 ± 0.44*10-3 mm2/s, p<0.05), but there was no significant difference between the WD and MD groups, MD and PD groups (all p>0.05). The D*mono, Dbi, and D*bi showed no significant difference among the three groups (all p>0.05). The area under the curve (AUC) of Dmono, fmono and fbi in differentiating WD from PD ESCC were 0.764, 0.961 and 0.932, and the sensitivity and specificity were 92.9% and 60%, 92.9% and 90%, 85.7% and 100%, respectively. The AUC of fmono and fbi in differentiating MD from PD ESCC were 0.839 and 0.757, and the sensitivity and specificity were 78.6% and 80%, 85.7% and 70%, respectively. The AUC of fmono and fbi in differentiating MD from WD ESCC were 0.746 and 0.740, and the sensitivity and specificity were 65% and 85%, 80% and 60%, respectively. The pathologically differentiated grade was correlated with all IVIM parameters (all p<0.05).ConclusionsThe mono-exponential IVIM model is superior to the bi-exponential IVIM model in differentiating pathological grades of ESCC, which may be a promising imaging method to predict pathological grades of ESCC.

Quantitative Diffusion-Weighted MR Imaging: Is There a Prognostic Role in Noninvasively Predicting the Histopathologic Type of Uveal Melanomas?

“…The ADC measurement derived from traditional DW sequences is burdened with some drawbacks, since it is obtained through a monoexponential model and is influenced by microcirculation perfusion. On the other hand, another DWI-based quantitative parameter, such as intravoxel incoherent motion (IVIM)-DWI, uses a biexponential model, thus discriminating information concerning pure water molecule diffusion from those regarding microcircu-lation perfusion and therefore separating the assessment of diffusion and perfusion in biological tissues [77][78][79][80][81].…”

Comparative evaluation of imaging methods for prognosis assessment in esophageal squamous cell carcinoma: focus on diffusion-weighted magnetic resonance imaging, computed tomography and esophagography

“…Additionally, due to the limitations of scanning conditions at that time, we did not collect data that could be used for advanced diffusion models, including IVIM and DKI, have to some extent corrected potential flaws in DWI, such as susceptibility to cellular or vascular system effects ( 41 ), or the influence of nonnormally distributed motion on ADC values ( 42 ). Many scholars have studied advanced diffusion models such as IVIM and have drawn meaningful conclusions ( 43 – 45 ). In the future, we will attempt to use advanced diffusion models such as IVIM and DKI to conduct more in-depth research in this field.…”

A comparison study of monoexponential and fractional order calculus diffusion models and 18F-FDG PET in differentiating benign and malignant solitary pulmonary lesions and their pathological types

“…The regression coefficients were estimated by using a maximum likelihood method ( 19 , 23 ). The area under the curve (AUC) given by the receiver operating characteristic (ROC) curve was used to evaluate the ability of different parameters to distinguish benign and malignant SPLs and different pathological types: (0.50, 0.70) indicated low diagnostic value, (0.70, 0.90) indicated moderate diagnostic value, and above 0.90 indicated high diagnostic value ( 25 ). The optimal cutoff value was selected according to the maximum value of the Youden Index and the corresponding accuracy, sensitivity and specificity values were calculated.…”