文章：

基于多参数磁共振成像与基线临床特征的密集多模态融合人工智能模型预测去势抵抗性前列腺癌进展

A Multiparametric MRI and Baseline-Clinical-Feature-Based Dense Multimodal Fusion Artificial Intelligence (MFAI) Model to Predict Castration-Resistant Prostate Cancer Progression

原文发布日期：3 May 2025

DOI: 10.3390/cancers17091556

类型: Article

开放获取: 是

英文摘要：

Objectives: The primary objective of this study was to identify whether patients with prostate cancer (PCa) could progress to denervation-resistant prostate cancer (CRPC) after 12 months of hormone therapy. Methods: A total of 96 PCa patients with baseline clinical data who underwent multiparametric magnetic resonance imaging (MRI) between September 2018 and September 2022 were included in this retrospective study. Patients were classified as progressing or not progressing to CRPC on the basis of their outcome after 12 months of hormone therapy. A dense multimodal fusion artificial intelligence (Dense-MFAI) model was constructed by incorporating a squeeze-and-excitation block and a spatial pyramid pooling layer into a dense convolutional network (DenseNet), as well as integrating the eXtreme Gradient Boosting machine learning algorithm. The accuracy, sensitivity, specificity, positive predictive value, negative predictive value, receiver operating characteristic curves, area under the curve (AUC) and confusion matrices were used as classification performance metrics. Results: The Dense-MFAI model demonstrated an accuracy of 94.2%, with an AUC of 0.945, when predicting the progression of patients with PCa to CRPC after 12 months of hormone therapy. The experimental validation demonstrated that combining radiomics feature mapping with baseline clinical characteristics significantly improved the model’s classification performance, confirming the importance of multimodal data. Conclusions: The Dense-MFAI model proposed in this study has the ability to more accurately predict whether a PCa patient could progress to CRPC. This model can assist urologists in developing the most appropriate treatment plan and prognostic measures.

摘要翻译： 

目的：本研究旨在探究前列腺癌（PCa）患者在接受12个月激素治疗后是否会进展为去势抵抗性前列腺癌（CRPC）。方法：本研究回顾性纳入了2018年9月至2022年9月期间接受多参数磁共振成像（MRI）检查且具有基线临床资料的96例PCa患者。根据患者接受12个月激素治疗后的结果，将其分为进展为CRPC组与未进展组。研究构建了一种密集多模态融合人工智能（Dense-MFAI）模型，该模型在密集卷积网络（DenseNet）中引入压缩激励模块与空间金字塔池化层，并整合了极限梯度提升机器学习算法。采用准确率、灵敏度、特异性、阳性预测值、阴性预测值、受试者工作特征曲线、曲线下面积（AUC）及混淆矩阵作为分类性能评价指标。结果：在预测PCa患者接受12个月激素治疗后进展为CRPC方面，Dense-MFAI模型展现出94.2%的准确率，AUC达0.945。实验验证表明，将影像组学特征映射与基线临床特征相结合可显著提升模型的分类性能，证实了多模态数据的重要性。结论：本研究提出的Dense-MFAI模型能够更精准地预测PCa患者是否可能进展为CRPC。该模型可协助泌尿科医师制定最适宜的治疗方案与预后管理策略。

原文链接：

A Multiparametric MRI and Baseline-Clinical-Feature-Based Dense Multimodal Fusion Artificial Intelligence (MFAI) Model to Predict Castration-Resistant Prostate Cancer Progression