文献类型：期刊文献

英文题名：Prediction of prostate cancer aggressiveness using magnetic resonance imaging radiomics: a dual-center study

作者：Pan, Nini[1];Shi, Liuyan[1];He, Diliang[1];Zhao, Jianxin[1];Xiong, Lianqiu[1];Ma, Lili[1];Li, Jing[1];Ai, Kai[2];Zhao, Lianping[3];Huang, Gang[3]

第一作者：Pan, Nini

通信作者：Huang, G[1]

机构：[1]Gansu Univ Chinese Med, Clin Med Coll 1, Lanzhou 730000, Gansu, Peoples R China;[2]Philips Healthcare, Clin & Tech Support, Xian, Peoples R China;[3]Gansu Prov Hosp, Dept Radiol, Lanzhou 730000, Gansu, Peoples R China

第一机构：甘肃中医药大学

通信机构：[1]corresponding author), Gansu Prov Hosp, Dept Radiol, Lanzhou 730000, Gansu, Peoples R China.

年份：2024

卷号：15

期号：1

外文期刊名：DISCOVER ONCOLOGY

收录：;Scopus(收录号：2-s2.0-85190463358);WOS:【SCI-EXPANDED(收录号:WOS:001203939700001)】；

基金：This study was supported by the Beijing Medical Award Foundation (YXJL-2022-0665-0197) and the grant from the Gansu Provincial Hospital (22GSSYD-33).

语种：英文

外文关键词：Prostate cancer; Magnetic resonance imaging; Radiomics; Aggressiveness; Gleason score; Positive needles

摘要：Purpose The Gleason score (GS) and positive needles are crucial aggressive indicators of prostate cancer (PCa). This study aimed to investigate the usefulness of magnetic resonance imaging (MRI) radiomics models in predicting GS and positive needles of systematic biopsy in PCa.Material and Methods A total of 218 patients with pathologically proven PCa were retrospectively recruited from 2 centers. Small-field-of-view high-resolution T2-weighted imaging and post-contrast delayed sequences were selected to extract radiomics features. Then, analysis of variance and recursive feature elimination were applied to remove redundant features. Radiomics models for predicting GS and positive needles were constructed based on MRI and various classifiers, including support vector machine, linear discriminant analysis, logistic regression (LR), and LR using the least absolute shrinkage and selection operator. The models were evaluated with the area under the curve (AUC) of the receiver-operating characteristic.Results The 11 features were chosen as the primary feature subset for the GS prediction, whereas the 5 features were chosen for positive needle prediction. LR was chosen as classifier to construct the radiomics models. For GS prediction, the AUC of the radiomics models was 0.811, 0.814, and 0.717 in the training, internal validation, and external validation sets, respectively. For positive needle prediction, the AUC was 0.806, 0.811, and 0.791 in the training, internal validation, and external validation sets, respectively.Conclusions MRI radiomics models are suitable for predicting GS and positive needles of systematic biopsy in PCa. The models can be used to identify aggressive PCa using a noninvasive, repeatable, and accurate diagnostic method.