Open AccessPredictive Ki-67 Proliferation Index of Cervical Squamous Cell Carcinoma Based on IVIM-DWI Combined with Texture Features
Author(s) -
Cuiping Li,
Meijun Zheng,
Xiaomin Zheng,
Xin Fang,
Junqiang Dong,
Chuanbin Wang,
Tingting Wang
Publication year - 2021
Publication title -
contrast media and molecular imaging/contrast media and molecular imaging
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.714
H-Index - 50
eISSN - 1555-4317
pISSN - 1555-4309
DOI - 10.1155/2021/8873065
Subject(s) - intravoxel incoherent motion , medicine , nuclear medicine , logistic regression , univariate , texture (cosmology) , diffusion mri , radiology , magnetic resonance imaging , multivariate statistics , mathematics , computer science , artificial intelligence , statistics , image (mathematics)
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/mm 2 ). The maximum level of CSCC with a b value of 800 sec/mm 2 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.