Zhang Yongsheng, Chen Wen, Yue Xianjie, Shen Jianliang, Gao Chen, Pang Peipei, Cui Feng, Xu Maosheng
The First Clinical Medical College of Zhejiang Chinese Medical University, Hangzhou, China.
Department of Radiology, The Guangxing Hospital Affiliated to Zhejiang Chinese Medical University, Hangzhou, China.
Front Oncol. 2020 Jun 30;10:888. doi: 10.3389/fonc.2020.00888. eCollection 2020.
To develop and validate a predictive model for discriminating clinically significant prostate cancer (csPCa) from clinically insignificant prostate cancer (ciPCa). This retrospective study was performed with 159 consecutively enrolled pathologically confirmed PCa patients from two medical centers. The dataset was allocated to a training group ( = 54) and an internal validation group ( = 22) from one center along with an external independent validation group ( = 83) from another center. A total of 1,188 radiomic features were extracted from T2WI, diffusion-weighted imaging (DWI), and apparent diffusion coefficient (ADC) images derived from DWI for each patient. Multivariable logistic regression analysis was performed to develop the model, incorporating the radiomic signature, ADC value, and independent clinical risk factors. This was presented using a radiomic nomogram. The receiver operating characteristic (ROC) curve was utilized to assess the predictive efficacy of the radiomic nomogram in both the training and validation groups. The decision curve analysis was used to evaluate which model achieved the most net benefit. The radiomic signature, which was made up of 10 selected features, was significantly associated with csPCa ( < 0.001 for both training and internal validation groups). The area under the curve (AUC) values of discriminating csPCa for the radiomics signature were 0.95 (training group), 0.86 (internal validation group), and 0.81 (external validation group). Multivariate logistic analysis identified the radiomic signature and ADC value as independent parameters of predicting csPCa. Then, the combination nomogram incorporating the radiomic signature and ADC value demonstrated a favorable classification capability with the AUC of 0.95 (training group), 0.93 (internal validation group), and 0.84 (external validation group). Appreciable clinical utility of this model was illustrated using the decision curve analysis for the nomogram. The nomogram, incorporating radiomic signature and ADC value, provided an individualized, potential approach for discriminating csPCa from ciPCa.
开发并验证一种用于区分临床显著性前列腺癌（csPCa）与临床非显著性前列腺癌（ciPCa）的预测模型。本回顾性研究纳入了来自两个医疗中心的159例经病理确诊的连续入组前列腺癌患者。数据集被分配为来自一个中心的训练组（=54）和内部验证组（=22），以及来自另一个中心的外部独立验证组（=83）。从每位患者的T2WI、扩散加权成像（DWI）以及由DWI得出的表观扩散系数（ADC）图像中提取了总共1188个影像组学特征。进行多变量逻辑回归分析以建立模型，纳入影像组学特征、ADC值和独立临床风险因素。这通过影像组学列线图呈现。利用受试者工作特征（ROC）曲线评估影像组学列线图在训练组和验证组中的预测效能。决策曲线分析用于评估哪种模型实现了最大净效益。由10个选定特征组成的影像组学特征与csPCa显著相关（训练组和内部验证组均P<0.001）。影像组学特征区分csPCa的曲线下面积（AUC）值分别为0.95（训练组）、0.86（内部验证组）和0.81（外部验证组）。多变量逻辑分析确定影像组学特征和ADC值为预测csPCa的独立参数。然后，结合影像组学特征和ADC值的联合列线图显示出良好的分类能力，AUC分别为0.95（训练组）、0.93（内部验证组）和0.84（外部验证组）。通过对列线图的决策曲线分析说明了该模型具有可观的临床实用性。结合影像组学特征和ADC值的列线图为区分csPCa和ciPCa提供了一种个性化的潜在方法。
