Title of article :
Predictive Ki-67 Proliferation Index of Cervical Squamous Cell Carcinoma Based on IVIM-DWI Combined with Texture Features
Author/Authors :
Li, Cuiping Department of Radiology - Anhui Provincial Hospital Affiliated to Anhui Medical University - Hefei, China , Zheng, Mingxue Graduate School - Bengbu Medical College - Bengbu - Anhui Province, China , Zheng, Xiaomin Department of Radiation Oncology - Anhui Provincial Hospital Affiliated To Anhui Medical University - Hefei, China , Fang, Xin Department of Radiology - First Affiliated Hospital - University of Science and Technology of China - Anhui Provincial Cancer Hospital - Hefei, China , Dong, Jiangning Department of Radiology - Anhui Provincial Hospital Affiliated to Anhui Medical University - Hefei, China , Wang, Chuanbin Department of Radiology - First Affiliated Hospital - University of Science and Technology of China - Anhui Provincial Cancer Hospital - Hefei, China , Wang, Tingting Department of Radiology - First Affiliated Hospital - University of Science and Technology of China - Anhui Provincial Cancer Hospital - Hefei, China
Abstract :
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.
Keywords :
Ki-67 , IVIM-DWI , Texture , CSCC
Journal title :
Contrast Media and Molecular Imaging
