文献类型：期刊文献

英文题名：Multiparametric MRI radiomics in prostate cancer for predicting Ki-67 expression and Gleason score: a multicenter retrospective study

作者：Zhou, Chuan[1];Zhang, Yun-Feng[2];Guo, Sheng[2];Wang, Dong[2];Lv, Hao-Xuan[1];Qiao, Xiao-Ni[5];Wang, Rong[1,4];Chang, De-Hui[6];Zhao, Li-Ming[7];Zhou, Feng-Hai[1,2,3]

第一作者：Zhou, Chuan

通信作者：Zhou, FH[1];Zhou, FH[2];Zhou, FH[3]

机构：[1]Lanzhou Univ, Clin Med Coll 1, Lanzhou 73000, Peoples R China;[2]Gansu Univ Chinese Med, Clin Med Coll 1, Lanzhou 730000, Peoples R China;[3]Gansu Prov Hosp, Dept Urol, Lanzhou 730000, Peoples R China;[4]Gansu Prov Hosp, Dept Nucl Med, Lanzhou 730000, Peoples R China;[5]Gansu Prov Hosp, Dept Informat Management, Lanzhou 730000, Peoples R China;[6]940 Hosp Joint Logist Support Force Chinese PLA, Dept Urol, Lanzhou 730000, Peoples R China;[7]Second Peoples Hosp Gansu Prov, Dept Urol, Lanzhou 730000, Peoples R China

第一机构：Lanzhou Univ, Clin Med Coll 1, Lanzhou 73000, Peoples R China

通信机构：[1]corresponding author), Lanzhou Univ, Clin Med Coll 1, Lanzhou 73000, Peoples R China;[2]corresponding author), Gansu Univ Chinese Med, Clin Med Coll 1, Lanzhou 730000, Peoples R China;[3]corresponding author), Gansu Prov Hosp, Dept Urol, Lanzhou 730000, Peoples R China.|[10735]甘肃中医药大学;

年份：2023

卷号：14

期号：1

外文期刊名：DISCOVER ONCOLOGY

收录：;Scopus(收录号：2-s2.0-85165346327);WOS:【SCI-EXPANDED(收录号:WOS:001032852900001)】；

语种：英文

外文关键词：Prostate cancer; Magnetic resonance imaging (MRI); Ki-67; Gleason score; Radiomics; Machine learning

摘要：PurposeProstate cancer (PCa) with high Ki-67 expression and high Gleason Scores (GS) tends to have aggressive clinicopathological characteristics and a dismal prognosis. In order to predict the Ki-67 expression status and the GS in PCa, we sought to construct and verify MRI-based radiomics signatures.Methods and materialsWe collected T2-weighted imaging (T2WI), diffusion-weighted imaging (DWI), and apparent diffusion coefficient (ADC) images from 170 PCa patients at three institutions and extracted 321 original radiomic features from each image modality. We used support vector machine (SVM) and least absolute shrinkage and selection operator (LASSO) logistic regression to select the most informative radiomic features and built predictive models using up sampling and feature selection techniques. Using receiver operating characteristic (ROC) analysis, the discriminating power of this feature was determined. Subsequent decision curve analysis (DCA) assessed the clinical utility of the radiomic features. The Kaplan-Meier (KM) test revealed that the radiomics-predicted Ki-67 expression status and GS were prognostic factors for PCa survival.ResultThe hypothesized radiomics signature, which included 15 and 9 selected radiomics features, respectively, was significantly correlated with pathological Ki-67 and GS outcomes in both the training and validation datasets. Areas under the curve (AUC) for the developed model were 0.813 (95% CI 0.681,0.930) and 0.793 (95% CI 0.621, 0.929) for the training and validation datasets, respectively, demonstrating discrimination and calibration performance. The model's clinical usefulness was verified using DCA. In both the training and validation sets, high Ki-67 expression and high GS predicted by radiomics using SVM models were substantially linked with poor overall survival (OS).ConclusionsBoth Ki-67 expression status and high GS correlate with PCa patient survival outcomes; therefore, the ability of the SVM classifier-based model to estimate Ki-67 expression status and the Lasso classifier-based model to assess high GS may enhance clinical decision-making.