Cuiping Li

Predictive Ki-67 Proliferation Index of Cervical Squamous Cell Carcinoma Based on IVIM-DWI Combined with Texture Features

Purpose. This study aims to determine whether IVIM-DWI combined with texture features based on preoperative IVIM-DWI could be used to predict the Ki-67 PI, which is a widely used cell proliferation biomarker in CSCC. Methods. A total of 70 patients were included. Among these patients, 16 patients were divided into the Ki-67 PI <50% group and 54 patients were divided into the Ki-67 PI ≥50% group based on the retrospective surgical evaluation. All patients were examined using a 3.0T MRI unit with one standard protocol, including an IVIM-DWI sequence with 10 b values (0–1,500 sec/mm2). The maximum level of CSCC with a b value of 800 sec/mm2 was selected. The parameters (diffusion coefficient (D), microvascular volume fraction (f), and pseudodiffusion coefficient (D ∗ )) were calculated with the ADW 4.6 workstation, and the texture features based on IVIM-DWI were measured using GE AK quantitative texture analysis software. The texture features included the first order, GLCM, GLSZM, GLRLM, and wavelet transform features. The differences in IVIM-DWI parameters and texture features between the two groups were compared, and the ROC curve was performed for parameters with group differences, and in combination. Results. The D value in the Ki-67 PI ≥50% group was lower than that in the Ki-67 PI <50% group ( P < 0.05 ). A total of 1,050 texture features were obtained using AK software. Through univariate logistic regression, mPMR feature selection, and multivariate logistic regression, three texture features were obtained: wavelet_HHL_GLRLM_ LRHGLE, lbp_3D_k_ firstorder_IR, and wavelet_HLH_GLCM_IMC1. The AUC of the prediction model based on the three texture features was 0.816, and the combined D value and three texture features was 0.834. Conclusions. Texture analysis on IVIM-DWI and its parameters was helpful for predicting Ki-67 PI and may provide a noninvasive method to investigate important imaging biomarkers for CSCC.

A Combination Analysis of IVIM‐DWI Biomarkers and T2WI‐Based Texture Features for Tumor Differentiation Grade of Cervical Squamous Cell Carcinoma

Purpose. To explore the value of intravoxel incoherent motion diffusion‐weighted imaging (IVIM‐DWI) and texture analysis on T2‐weighted imaging (T2WI) for evaluating pathological differentiation of cervical squamous cell carcinoma. Method. This retrospective study included a total of 138 patients with pathologically confirmed poor/moderate/well‐differentiated (71/49/18) who underwent conventional MRI and IVIM‐DWI scans. The values of ADC, D, D∗, and f and 58 T2WI‐based texture features (18 histogram features, 24 gray‐level co‐occurrence matrix features, and 16 gray‐level run length matrix features) were obtained. Multiple comparison, correlation, and regression analyses were used. Results. For IVIM‐DWI, the ADC, D, D∗, and f were significantly different among the three groups (p < 0.05). ADC, D, and D∗ were positively correlated with pathological differentiation (r = 0.262, 0.401, 0.401; p < 0.05), while the correlation was negative for f (r = −0.221; p < 0.05). The comparison of 52 parameters of texture analysis on T2WI reached statistically significant levels (p < 0.05). Multivariate logistic regression analysis incorporated significant IVIM‐DWI, and texture features on T2WI showed good diagnostic performance both in the four differentiation groups (poorly vs. moderately, area under the curve(AUC) = 0.797; moderately vs. well, AUC = 0.954; poorly vs. moderately and well, AUC = 0.795; and well vs. moderately and poorly, AUC = 0.952). The AUCs of each parameters alone were smaller than that of each regression model (0.503∼0.684, 0.547∼0.805, 0.511∼0.712, and 0.636∼0.792, respectively; pairwise comparison of ROC curves between regression model and individual variables, p < 0.05). Conclusions. IVIM‐DWI biomarkers and T2WI‐based texture features had potential to evaluate the pathological differentiation of cervical squamous cell carcinoma. The combination of IVIM‐DWI with texture analysis improved the predictive performance.