Department of Radiology, The Second Affiliated Hospital of Chongqing Medical University, No. 76, Linjiang Road, Yuzhong District, Chongqing, 400000, China.
Basic Medical College of Chongqing Medical University, No. 1 Medical School Road, Yuzhong District, Chongqing, 400042, China.
Clin Radiol. 2019 Nov;74(11):896.e1-896.e8. doi: 10.1016/j.crad.2019.07.011. Epub 2019 Sep 5.
To investigate whether the combination of radiomics and automatic machine learning-based classification of original images from multiphase dynamic contrast-enhanced (DCE)-magnetic resonance imaging (MRI) can predict prostate cancer (PCa) aggressiveness before biopsy.
Forty consecutive biopsy-confirmed PCa patients were included. Biopsy was performed within 4 weeks after the DCE-MRI examinations. According to the time-signal-intensity curve, lesion segmentation was performed on the first and on the strongest phase of the enhancement on the original DCE-MRI images, and 1,029 quantitative radiomics features were calculated automatically from each lesion, wherein there were three datasets available (Dataset-F, Dataset-S and Dataset-FS). The variance threshold method, select k-best method and least absolute shrinkage and selection operator (LASSO) algorithm were used to reduce the feature dimensions. Five machine learning approaches leveraging cross-validation were employed, and the clinical value of each model was evaluated by area under the receiver operating characteristic curve (AUC). Correlation analysis was performed between the features of the machine learning model that achieved the best classification performance and the Gleason score (GS) of the PCa lesion.
Eight, four, and 16 features were selected as optimal subsets in Dataset-F, -S and -FS, respectively. Among all three datasets, logistic regression (LR)-based analysis with Dataset-FS had the highest predication efficacy (AUC=0.93). Ten features in Dataset-FS showed significantly positively correlation with GS. The model performance of Dataset-F was generally better than that in Dataset-S.
A combination of radiomics and machine learning-analysis based analysis of the union of the first and strongest phases of original DCE-MRI images can predict PCa aggressiveness non-invasively, accurately, and automatically.
探讨多期动态对比增强（DCE）-磁共振成像（MRI）原始图像的放射组学与基于自动机器学习的分类相结合，是否能在活检前预测前列腺癌（PCa）的侵袭性。
连续纳入 40 例经活检证实的 PCa 患者。DCE-MRI 检查后 4 周内行活检。根据时间-信号强度曲线，在原始 DCE-MRI 图像的第 1 期和增强最强期对病变进行分割，并从每个病变自动计算 1029 个定量放射组学特征，其中有 3 组数据集（Dataset-F、Dataset-S 和 Dataset-FS）可用。采用方差阈值法、选择 k-最佳法和最小绝对收缩和选择算子（LASSO）算法进行特征降维。采用 5 种基于交叉验证的机器学习方法，通过受试者工作特征曲线下面积（AUC）评估每个模型的临床价值。对具有最佳分类性能的机器学习模型的特征与 PCa 病变的 Gleason 评分（GS）进行相关性分析。
在 Dataset-F、-S 和 -FS 中，分别选择了 8、4 和 16 个特征作为最优子集。在这 3 组数据集当中，基于 Dataset-FS 的逻辑回归（LR）分析预测效能最高（AUC=0.93）。在 Dataset-FS 中，10 个特征与 GS 呈显著正相关。Dataset-F 的模型性能普遍优于 Dataset-S。
联合应用原始 DCE-MRI 图像的第 1 期和最强期的放射组学与机器学习分析，可以无创、准确、自动地预测 PCa 的侵袭性。
