Liu Xiang, Zhang Zhong-Xin, Zheng Bing, Xu Min, Cao Xin-Yu, Huang Hai-Ming
Department of Ultrasound, The Second Affiliated Hospital of Nantong University, Nantong, Jiangsu, China.
Department of Urology Surgery, The Second Affiliated Hospital of Nantong University, Nantong, Jiangsu, China.
Front Oncol. 2025 Apr 8;15:1538854. doi: 10.3389/fonc.2025.1538854. eCollection 2025.
This study aimed to establish and evaluate a model utilizing bi-parametric ultrasound-based deep learning radiomics (DLR) in conjunction with clinical factors to anticipate clinically significant prostate cancer (csPCa).
We retrospectively analyzed 232 participants from our institution who underwent both B-mode ultrasound and shear wave elastography (SWE) prior to prostate biopsy between June 2022 and December 2023. A random allocation placed the participants into training and test cohorts with a 7:3 distribution. We developed a nomogram that integrates DLR with clinical factors within the training cohort, which was subsequently validated using the test cohort. The diagnostic performance and clinical applicability were evaluated with receiver operating characteristic (ROC) curve analysis and decision curve analysis.
In our study, the bi-parametric ultrasound-based DLR model demonstrated an area under the curve (AUC) of 0.80 (95%CI: 0.70-0.91) in the test set, surpassing the performance of both the radiomics and deep learning models individually. By integrating clinical factors, a composite model, presented as the nomogram, was developed and exhibited superior diagnostic performance, achieving an AUC of 0.87 (95%CI: 0.77-0.95) in the test set. The performance exceeded that of the DLR ( = 0.049) and the clinical model (AUC = 0.79, 95%CI: 0.69-0.86, = 0.041). Furthermore, the decision curve analysis indicated that the composite model provided a greater net benefit across a various high-risk threshold than the DLR or the clinical model alone.
To our knowledge, this is the first proposal of a nomogram integrating ultrasound-based DLR with clinical indicators for predicting csPCa. This nomogram can improve the accuracy of csPCa prediction and may help physicians make more confident decisions regarding interventions, particularly in settings where MRI is unavailable.
本研究旨在建立并评估一种利用基于双参数超声的深度学习放射组学（DLR）结合临床因素来预测临床显著性前列腺癌（csPCa）的模型。
我们回顾性分析了2022年6月至2023年12月期间在我院接受前列腺活检前进行B超和剪切波弹性成像（SWE）检查的232名参与者。通过随机分配将参与者按7:3的比例分为训练组和测试组。我们在训练组中开发了一个将DLR与临床因素相结合的列线图，随后使用测试组进行验证。通过受试者操作特征（ROC）曲线分析和决策曲线分析评估诊断性能和临床适用性。
在我们的研究中，基于双参数超声的DLR模型在测试集中的曲线下面积（AUC）为0.80（95%CI：0.70 - 0.91），超过了单独的放射组学和深度学习模型的性能。通过整合临床因素，开发了一个以列线图形式呈现的复合模型，该模型在测试集中表现出卓越的诊断性能，AUC为0.87（95%CI：0.77 - 0.95）。其性能超过了DLR（P = 0.049）和临床模型（AUC = 0.79，95%CI：0.69 - 0.86，P = 0.041）。此外，决策曲线分析表明，复合模型在各种高风险阈值下比单独的DLR或临床模型提供了更大的净效益。
据我们所知，这是首次提出将基于超声的DLR与临床指标相结合用于预测csPCa的列线图。该列线图可提高csPCa预测的准确性，并可能有助于医生在干预决策时更有信心，特别是在无法进行MRI检查的情况下。
