We developed a novel machine-learning algorithm to augment the clinical diagnosis of prostate cancer utilizing first and second-order texture analysis metrics in a novel application of machine-learning radiomics analysis. We successfully discriminated between significant prostate cancers versus non-tumor regions and provided accurate prediction between Gleason score cohorts with statistical sensitivity of 0.82, 0.81 and 0.91 in three separate pathology classifications. Tumor heterogeneity and prediction of the Gleason score were quantified using two feature selection approaches and two separate classifiers with tuned hyperparameters. There was a total of 71 patients analyzed in this study. Multiparametric MRI, incorporating T2WI and ADC maps, were used to derive radiomics features. Recursive feature elimination (RFE), the least absolute shrinkage and selection operator (LASSO), and two classification approaches, incorporating a support vector machine (SVM) (with randomized search) and random forest (RF) (with grid search), were utilized to differentiate between non-tumor regions and significant cancer while also predicting the Gleason score. In T2WI images, the RFE feature selection approach combined with RF and SVM classifiers outperformed LASSO with SVM and RF classifiers. The best performance was achieved by combining LASSO and SVM into a model that used both T2WI and ADC images. This model had an area under the curve (AUC) of 0.91. Radiomic features computed from ADC and T2WI images were used to predict three groups of Gleason score using two kinds of feature selection methods (RFE and LASSO), RF and SVM classifier models with tuned hyperparameters. Using combined sequences (T2WI and ADC map images) and combined radiomics (1st and GLCM features), LASSO, with a feature selection method with RF, was able to predict G3 with the highest sensitivity at a level AUC of 0.92. To predict G3 for single sequence (T2WI images) using GLCM features, LASSO with SVM achieved the highest sensitivity with an AUC of 0.92.
我们开发了一种新型机器学习算法，通过首次应用机器学习放射组学分析中的一阶和二阶纹理分析指标，以增强前列腺癌的临床诊断。该算法成功区分了显著性前列腺癌与非肿瘤区域，并在三种独立的病理分类中对格里森评分组实现了准确预测，统计灵敏度分别达到0.82、0.81和0.91。研究采用两种特征选择方法和两个经超参数调优的分类器，对肿瘤异质性及格里森评分预测进行了量化分析。本研究共纳入71例患者，基于包含T2加权成像和表观扩散系数图的多参数磁共振成像提取放射组学特征。 通过递归特征消除法、最小绝对收缩与选择算子两种特征选择方法，结合支持向量机（采用随机搜索）和随机森林（采用网格搜索）两种分类模型，实现了非肿瘤区域与显著性癌症的鉴别诊断及格里森评分预测。在T2加权成像中，递归特征消除法结合随机森林与支持向量机分类器的组合表现优于最小绝对收缩与选择算子结合支持向量机与随机森林分类器。最佳性能模型为最小绝对收缩与选择算子结合支持向量机并联合使用T2加权成像与表观扩散系数图数据，其曲线下面积达到0.91。 研究采用表观扩散系数图和T2加权成像计算的放射组学特征，通过递归特征消除法和最小绝对收缩与选择算子两种特征选择方法，结合经超参数调优的随机森林与支持向量机分类模型，成功预测了三组格里森评分。在联合序列（T2加权成像与表观扩散系数图）和组合放射组学特征（一阶与灰度共生矩阵特征）条件下，最小绝对收缩与选择算子结合随机森林特征选择方法对G3分级的预测效能最优，曲线下面积达0.92。而在单一序列（T2加权成像）使用灰度共生矩阵特征时，最小绝对收缩与选择算子结合支持向量机模型对G3分级的预测灵敏度最高，曲线下面积同样达到0.92。
肿瘤(癌症)患者之家