Liu Yang
X-Ray Department, The No.1 People's Hospital of Huzhou, Huzhou, Zhejiang, 313000, People's Republic of China.
Int J Gen Med. 2023 Jul 17;16:3043-3051. doi: 10.2147/IJGM.S419039. eCollection 2023.
To explore the value of the magnetic resonance imaging (MRI) radiomics model in predicting prostate cancer (PCa) invasion.
Clinical data of 86 pathologically confirmed PCa patients in our hospital were collected, including 44 cases in the invasive group and 42 cases in the non-invasive group. All patients underwent MRI examinations, and the same parameters were used. The lesion area was manually delineated and the radiomics features were extracted from T2WI. The radiomics signature based on LASSO regression was established. Besides, logistic regression was used to identify independent clinical predictors, and a combined model incorporating the radiomics signature and independent clinical risk factor was constructed. Finally, the receiver operating characteristic curve (ROC) analysis and decision curve analysis (DCA) was performed to compare the prediction efficiency and clinical benefit of each model.
A total of 867 radiomics features were obtained, and six of them were incorporated into the radiomics model. Multivariate logistic regression analysis exhibited the Gleason score as an independent clinical risk factor for PCa invasion. ROC results showed that the performance of the radiomics model was comparable to that of the clinical-radiomics model in predicting PCa invasion, and it was better than that of the single Gleason score. DCA also confirmed the considerable clinical application value of the radiomics and the clinical-radiomics models.
As a simple, non-invasive, and efficient method, the radiomics model has important predictive value for PCa invasion.
探讨磁共振成像（MRI）影像组学模型在预测前列腺癌（PCa）侵袭方面的价值。
收集我院86例经病理证实的PCa患者的临床资料，其中侵袭组44例，非侵袭组42例。所有患者均接受MRI检查，采用相同参数。手动勾勒病变区域，并从T2WI中提取影像组学特征。建立基于LASSO回归的影像组学特征。此外，采用逻辑回归确定独立的临床预测因素，并构建包含影像组学特征和独立临床危险因素的联合模型。最后，进行受试者工作特征曲线（ROC）分析和决策曲线分析（DCA），以比较各模型的预测效率和临床获益。
共获得867个影像组学特征，其中6个被纳入影像组学模型。多因素逻辑回归分析显示Gleason评分是PCa侵袭的独立临床危险因素。ROC结果显示，影像组学模型在预测PCa侵袭方面的性能与临床-影像组学模型相当，且优于单一的Gleason评分。DCA也证实了影像组学模型和临床-影像组学模型具有可观的临床应用价值。
作为一种简单、无创且高效的方法，影像组学模型对PCa侵袭具有重要的预测价值。
