Diagnostic value of radiomics based on biparametric prostate MRI imaging in Gleason classification of prostate cancer

医学 前列腺癌 前列腺 放射科 无线电技术 接收机工作特性 前列腺切除术 前列腺活检 多参数磁共振成像 磁共振成像 核医学
作者
Hongtao Zhang,Zeyu Hu,Haiyi Wang,Bo Wang,Xu Bai,Huiyi Ye
出处
期刊:Chinese journal of radiology 卷期号:53 (10): 849-852
标识
DOI:10.3760/cma.j.issn.1005-1201.2019.10.011
摘要

Objective To explore the value of radiomics in stratifying the Gleason score (GS) of prostate cancer based on vast image features from biparametric MRI. Methods Three hundred and sixteen patients were enrolled in this study from October, 2015 to December, 2018 and their results of surgical pathology were obtained. The lesions were manually depicted by 3D-Slicer. Then, 106-dimensional features extracted by radiomics were used to conduct Spearman non-parametric correlation test with the high and low risk stratification of GS. The constructed Neural Network was trained with the features after dimension reduction by principal component analysis as the input. Then, the testing set was fed in to get the predictive capability of the model. In the end, 10-fold cross-validation and shuffle of 100 times were used to test the accuracy of the prediction and the generalization ability of the model. Results Seventy seven-dimensional features with significant correlation were found at the level of P valued=0.05 (two-tailed). After dimensional features were reduced, 21 dimensional new feature spaces with 99% original feature information were obtained. The results on the testing data after the 10-fold validation and shuffle were AUC=0.712 with T2WI, AUC=0.689 with DWI (b=1 000 s/mm2), AUC=0.689 with DWI (b=2 000 s/mm2) and AUC=0.691 with DWI (b=3 000 s/mm2). Conclusion The neural network after extracting features from biparametric MRI images can accurately and automatically distinguish the high risk and low risk groups of Gleason grade of prostatic cancer. Key words: Prostatic neoplasms; Magnetic resonance imaging; Radiomics
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