Chen Tong, Hu Wei, Zhang Yueyue, Wei Chaogang, Zhao Wenlu, Shen Xiaohong, Zhang Caiyuan, Shen Junkang
Department of Radiology, The Second Affiliated Hospital of Soochow University, Suzhou 215000, China (T.C., Y.Z., C.W., W.Z., X.S., C.Z., J.S.).
Department of Radiology, Taihu Sanatorium of Jiangsu Province, Wuxi 214000, China (W.H.).
Acad Radiol. 2025 Feb;32(2):864-876. doi: 10.1016/j.acra.2024.10.009. Epub 2024 Nov 3.
To establish a multimodal deep learning nomogram for predicting clinically significant prostate cancer in patients with gray-zone PSA levels.
This retrospective study enrolled 303 patients with pathological results between January 2018 and December 2022. Clinical variables and the PI-RADS v2.1 score were used to construct a clinical model. Radiomics and deep learning features from bp-MRI were used to develop a radiomics model with SVM and a deep learning model, respectively. A hybrid fusion approach was used to integrate the multimodal data and construct combined models (Comb.Rad.model and Comb.DL.model). The robustness of the radiomics model with XGBoost was validated and compared. Model efficacy was assessed through ROC curve and decision curve analysis. A nomogram was developed based on the best-performing model.
The clinical model had AUCs of 0.845 and 0.779 in the training and testing set. The radiomics model with SVM and the deep learning model achieved AUCs of 0.825 and 0.933 in the training set and 0.811 and 0.907 in the testing set, respectively. The diagnostic performance of the combined models was significantly improved, with Comb.DL.model having a higher AUC than Comb.Rad.model in both the training (0.986 vs. 0.924, P = 0.008) and testing (0.965 vs. 0.859, P = 0.005) set. The diagnostic efficiency of both the radiomics model and Comb.Rad.model with XGBoost were comparable to that of SVM, confirming the robustness of the established model.
The integrated nomogram combining deep learning features, PI-RADS score, and clinical variables significantly outperformed the traditional radiomics and clinical models.
建立一种多模态深度学习列线图，用于预测前列腺特异抗原（PSA）水平处于灰色区域的患者是否患有具有临床意义的前列腺癌。
这项回顾性研究纳入了2018年1月至2022年12月期间303例有病理结果的患者。临床变量和前列腺影像报告和数据系统（PI-RADS）v2.1评分用于构建临床模型。分别利用基于bp-MRI的放射组学和深度学习特征，通过支持向量机（SVM）构建放射组学模型，通过深度学习构建深度学习模型。采用混合融合方法整合多模态数据并构建联合模型（Comb.Rad.model和Comb.DL.model）。对基于极端梯度提升（XGBoost）的放射组学模型的稳健性进行验证和比较。通过ROC曲线和决策曲线分析评估模型效能。基于表现最佳的模型制定列线图。
临床模型在训练集和测试集的曲线下面积（AUC）分别为0.845和0.779。基于SVM的放射组学模型和深度学习模型在训练集的AUC分别为0.825和0.933，在测试集的AUC分别为0.811和0.907。联合模型的诊断性能显著提高，Comb.DL.model在训练集（0.986对0.924，P = 0.008）和测试集（0.965对0.859，P = 0.005）的AUC均高于Comb.Rad.model。基于XGBoost的放射组学模型和Comb.Rad.model的诊断效率与SVM相当，证实了所建模型的稳健性。
结合深度学习特征、PI-RADS评分和临床变量的综合列线图显著优于传统放射组学模型和临床模型。
