Department of Urology, Fujian Union Hospital, Fujian Medical University, Fuzhou, China.
Department of Urology, The Second Affiliated Hospital of Fujian University of Traditional Chinese Medicine, Fuzhou, China.
J Int Med Res. 2024 Oct;52(10):3000605241275338. doi: 10.1177/03000605241275338.
Radiomics models have demonstrated good performance for the diagnosis and evaluation of prostate cancer (PCa). However, there are currently no validated imaging models that can predict PCa or clinically significant prostate cancer (csPCa). Therefore, we aimed to identify the best such models for the prediction of PCa and csPCa.
We performed a retrospective study of 942 patients with suspected PCa before they underwent prostate biopsy. MRI data were collected to manually segment suspicious regions of the tumor layer-by-layer. We then constructed models using the extracted imaging features. Finally, the clinical value of the models was evaluated.
A diffusion-weighted imaging (DWI) plus apparent diffusion coefficient (ADC) random-forest model and a T2-weighted imaging plus ADC and DWI multilayer perceptron model were the best models for the prediction of PCa and csPCa, respectively. Areas under the curve (AUCs) of 0.942 and 0.999, respectively, were obtained for a training set. Internal validation yielded AUCs of 0.894 and 0.605, and external validation yielded AUCs of 0.732 and 0.623.
Models based on machine learning comprising radiomic features and clinical indicators showed good predictive efficiency for PCa and csPCa. These findings demonstrate the utility of radiomic models for clinical decision-making.
放射组学模型已在前列腺癌（PCa）的诊断和评估中表现出良好的性能。然而，目前尚无经过验证的影像学模型可用于预测 PCa 或临床显著前列腺癌（csPCa）。因此，我们旨在确定用于预测 PCa 和 csPCa 的最佳模型。
我们对 942 名疑似 PCa 患者进行了回顾性研究，这些患者在接受前列腺活检之前接受了 MRI 数据采集，以手动逐层对肿瘤可疑区域进行分割。然后，我们使用提取的影像学特征构建模型。最后，评估了模型的临床价值。
扩散加权成像（DWI）加表观扩散系数（ADC）随机森林模型和 T2 加权成像加 ADC 和 DWI 多层感知机模型分别是预测 PCa 和 csPCa 的最佳模型，训练集的曲线下面积（AUC）分别为 0.942 和 0.999。内部验证得到的 AUC 分别为 0.894 和 0.605，外部验证得到的 AUC 分别为 0.732 和 0.623。
基于机器学习的放射组学特征和临床指标模型对 PCa 和 csPCa 具有良好的预测效率。这些发现证明了放射组学模型在临床决策中的实用性。
