磁共振影像组学在鉴别中低危和高危前列腺癌中的应用

目的建立基于支持向量机学习算法的影像组学模型，研究其鉴别高危前列腺癌与中低危前列腺癌的诊断效能。方法回顾性分析265例经病理证实的前列腺癌病人，其中高危病人155例，中低危病人110例。所有病人术前均进行MRI检查。由两位放射医师使用达尔文智能科研平台手动勾画感兴趣区，从每例病人的T2WI和ADC图中分别提取影像组学特征，采用受试者工作特征(ROC)曲线及ROC曲线下面积(AUC)验证影像组学特征的鉴别效能，对比T2WI、ADC及T2WI+ADC的诊断价值。结果共筛选出10个影像组学特征(6个ADC序列特征，4个T2WI序列特征)可以用来鉴别高危及中低危前列腺癌。仅使用T2WI获得的组学模型鉴别效能较低，训练队列AUC为0.70(95%CI 0.63~0.77)，验证队列AUC为0.58(95%CI 0.47~0.68)。ADC图组学模型预测效能较好，训练队列AUC为0.79(95%CI 0.72~0.85)，验证队列AUC为0.78(95%CI 0.68~0.86)。T2WI联合ADC图构建的影像组学模型表现出最优预测效能，训练队列AUC为0.84(95%CI 0.78~0.89)，验证队列AUC为0.80(95%CI 0.69~0.88)。结论本研究构建的基于T2WI和ADC图的影像组学模型在一定程度上对中低危及高危前列腺癌病人进行区分，为前列腺癌的分期提供了一种无创的预测方式，指导治疗方案的选择。

Application of magnetic resonance imaging in differentiating low-and medium-risk prostate cancer from high-risk prostate cancer

ObjectiveTo establish a radiomic model based on SVM learning algorithm to evaluate the diagnostic efficiency in differentiating high-risk prostate cancer(PCa) from low-and medium-risk PCa.Methods265 patients with PCa confirmed by histopathologic results were analyzed retrospectively, including 155 high risk patients and 110 low-and medium-risk patients.All patients were examined by MRI before operation.Regions of interest(ROIs)were manually delineated by two radiologists using DARWIN research platform, and the radiomic features were extracted from each segmented ROI of the T2WI and ADC images.The Receiver Operating Characteristic(ROC) curve and the area under the ROC curve(AUC) were used to validate the differential diagnosticefficiency of radiomic features, and the diagnostic performance of T2WI, ADC and T2WI+ADC were compared.ResultsA total of 10 radiomic features were selected to identify high-risk, low-and medium-risk prostate cancer.The classification performance of the T2WI-based radiomic model was not satisfying with an AUC of 0.70(95%CI 0.63-0.77) in the training set and 0.58(95%CI 0.47-0.68) in the validation set.ADC-based model performed better with the training set achieved AUC of 0.79(95%CI 0.72-0.85) and 0.78(95%CI 0.68-0.86) of the validation set.The ensemble model constructed by both T2WI and ADC achieved the highest predictive AUCs, 0.84(95%CI 0.78-0.89) for training set and 0.80(95%CI 0.69-0.88) of the validation.ConclusionsThe radiomic model based on T2WI and ADC maps distinguished patients with different levels of prostate cancer risk to a certain extent, which provides a non-invasive prediction method for the classification and treatment guidance of Pca.