Jin Pengfei, Yang Liqin, Qiao Xiaomeng, Hu Chunhong, Hu Chenhan, Wang Ximing, Bao Jie
Department of Radiology, The First Affifiliated Hospital of Soochow University, Suzhou, China.
Institute of Medical Imaging, Soochow University, Suzhou, China.
Front Oncol. 2022 Feb 24;12:840786. doi: 10.3389/fonc.2022.840786. eCollection 2022.
To determine the predictive performance of the integrated model based on clinical factors and radiomic features for the accurate identification of clinically significant prostate cancer (csPCa) among Prostate Imaging Reporting and Data System (PI-RADS) 3 lesions.
A retrospective study of 103 patients with PI-RADS 3 lesions who underwent pre-operative 3.0-T MRI was performed. Patients were randomly divided into the training set and the testing set at a ratio of 7:3. Radiomic features were extracted from axial T2WI, diffusion-weighted imaging (DWI), and apparent diffusion coefficient (ADC) images of each patient. The minimum redundancy maximum relevance (mRMR) and least absolute shrinkage and selection operator (LASSO) feature selection methods were used to identify the radiomic features and construct a radiomic model for csPCa identification. Moreover, multivariable logistic regression analysis was used to integrate the clinical factors with radiomic feature model to further improve the accuracy of csPCa identification, and the two are presented in the form of normogram. The performance of the integrated model was compared with radiomic model and clinical model on testing set.
A total of four radiomic features were selected and used for radiomic model construction producing a radiomic score (Radscore). Radscore was significantly different between the csPCa and the non-csPCa patients (training set: < 0.001; testing set: = 0.035). Multivariable logistic regression analysis showed that age and PSA could be used as independent predictors for csPCa identification. The clinical-radiomic model produced the receiver operating characteristic (ROC) curve (AUC) in the testing set was 0.88 (95%CI, 0.75-1.00), which was similar to clinical model (AUC = 0.85; 95%CI, 0.52-0.90) ( = 0.048) and higher than the radiomic model (AUC = 0.71; 95%CI, 0.68-1.00) ( < 0.001). The decision curve analysis implies that the clinical-radiomic model could be beneficial in identifying csPCa among PI-RADS 3 lesions.
The clinical-radiomic model could effectively identify csPCa among biparametric PI-RADS 3 lesions and thus could help avoid unnecessary biopsy and improve the life quality of patients.
基于临床因素和影像组学特征确定综合模型对前列腺影像报告和数据系统（PI-RADS）3类病变中具有临床意义的前列腺癌（csPCa）进行准确识别的预测性能。
对103例接受术前3.0-T MRI检查的PI-RADS 3类病变患者进行回顾性研究。患者按7:3的比例随机分为训练集和测试集。从每位患者的轴向T2WI、扩散加权成像（DWI）和表观扩散系数（ADC）图像中提取影像组学特征。采用最小冗余最大相关（mRMR）和最小绝对收缩与选择算子（LASSO）特征选择方法来识别影像组学特征并构建用于识别csPCa的影像组学模型。此外，使用多变量逻辑回归分析将临床因素与影像组学特征模型相结合，以进一步提高csPCa识别的准确性，并以列线图的形式呈现两者。在测试集上比较综合模型与影像组学模型和临床模型的性能。
共选择了四个影像组学特征用于构建影像组学模型并产生影像组学评分（Radscore）。csPCa患者与非csPCa患者的Radscore存在显著差异（训练集：<0.001；测试集：=0.035）。多变量逻辑回归分析表明，年龄和前列腺特异抗原（PSA）可作为识别csPCa的独立预测因子。临床-影像组学模型在测试集中产生的受试者操作特征（ROC）曲线下面积（AUC）为0.88（95%CI，0.75-1.00），与临床模型（AUC = 0.85；95%CI，0.52-0.90）（=0.048）相似且高于影像组学模型（AUC = 0.71；95%CI，0.68-1.00）（<0.001）。决策曲线分析表明，临床-影像组学模型有助于在PI-RADS 3类病变中识别csPCa。
临床-影像组学模型能够有效识别双参数PI-RADS 3类病变中的csPCa，从而有助于避免不必要的活检并提高患者生活质量。
