To investigate the efficiency of radiomics signature in discriminating between benign and malignant prostate lesions with similar biparametric magnetic resonance imaging (bp-MRI) findings.
Our study consisted of 331 patients underwent bp-MRI before pathological examination from January 2013 to November 2016. Radiomics features were extracted from peripheral zone (PZ), transition zone (TZ), and lesion areas segmented on images obtained by T2-weighted imaging (T2WI), diffusion-weighted imaging (DWI), and its derivative apparent-diffusion coefficient (ADC) imaging. The individual prediction model, built using the clinical data and biparametric MRI features (Bp signature), was prepared using data of 232 patients and validated using data of 99 patients. The predictive performance was calculated and demonstrated using receiver operating characteristic (ROC) curves, calibration curves, and decision curves.
The Bp signature, based on the six selected radiomics features of bp-MRI, showed better discrimination in the validation cohort (area under the curve [AUC], 0.92) than on each subcategory images (AUC, 0.81 on T2WI; AUC, 0.77 on DWI; AUC, 0.89 on ADC). The differential diagnostic efficiency was poorer with the clinical model (AUC, 0.73), built using the selected independent clinical risk factors with statistical significance (P < 0.05), than with the Bp signature. Discrimination efficiency improved when including the Bp signature and clinical factors [i.e., the individual prediction model (AUC, 0.93)].
The Bp signature, based on bp-MRI, performed better than each single imaging modality. The individual prediction model including the radiomics signatures and clinical factors showed better preoperative diagnostic performance, which could contribute to clinical individualized treatment.
European journal of radiology. 2019 Feb 25 [Epub]
Min Xu, Mengjie Fang, Jian Zou, Shudong Yang, Dongdong Yu, Lianzhen Zhong, Chaoen Hu, Yali Zang, Di Dong, Jie Tian, Xiangming Fang
Imaging Center, Wuxi People's Hospital, Nanjing Medical University, No. 299, Qingyang Road, Liangxi District, Wuxi, Jiangsu Province, 214023, China; CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China., CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China; University of Chinese Academy of Sciences, Beijing, 100190, China., Center of Clinical Research, Wuxi People's Hospital, Nanjing Medical University, No. 299, Qingyang Road, Liangxi District, Wuxi, Jiangsu Province, 214023, China., Department of Pathology, Wuxi People's Hospital, Nanjing Medical University, No. 299, Qingyang Road, Liangxi District, Wuxi, Jiangsu Province, 214023, China., CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China; University of Chinese Academy of Sciences, Beijing, 100190, China; Beijing Key Laboratory of Molecular Imaging, Beijing 100190, China. Electronic address: ., CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China; University of Chinese Academy of Sciences, Beijing, 100190, China; Beijing Key Laboratory of Molecular Imaging, Beijing 100190, China. Electronic address: ., Imaging Center, Wuxi People's Hospital, Nanjing Medical University, No. 299, Qingyang Road, Liangxi District, Wuxi, Jiangsu Province, 214023, China. Electronic address: .