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.
Eur J Radiol. 2019 May;114:38-44. doi: 10.1016/j.ejrad.2019.02.032. Epub 2019 Feb 25.
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.
研究基于多参数磁共振成像（bp-MRI）表现相似的影像组学特征对前列腺良恶性病变的鉴别效能。
本研究纳入了 2013 年 1 月至 2016 年 11 月间行 bp-MRI 检查并经病理证实的 331 例患者。从 T2 加权成像（T2WI）、弥散加权成像（DWI）及其衍生的表观弥散系数（ADC）图像中提取外周带（PZ）、移行带（TZ）和病灶区的影像组学特征。采用 232 例患者的临床及 bp-MRI 数据构建个体化预测模型（Bp 模型），并采用另外 99 例患者的数据进行验证。采用受试者工作特征（ROC）曲线、校准曲线和决策曲线评估预测效能。
基于 bp-MRI 中 6 个影像组学特征的 Bp 模型在验证组中的鉴别效能优于各亚类图像（T2WI：AUC 为 0.81；DWI：AUC 为 0.77；ADC：AUC 为 0.89）。与 Bp 模型相比，基于有统计学意义的 6 个独立临床危险因素的临床模型（AUC 为 0.73）的鉴别效能较差。当包括 Bp 模型和临床因素时（即个体化预测模型，AUC 为 0.93），诊断效能有所提高。
基于 bp-MRI 的 Bp 模型优于单一成像模态。包括影像组学特征和临床因素的个体化预测模型具有更好的术前诊断效能，有助于实现临床个体化治疗。
