Department of Biomedical Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, People's Republic of China.
Department of Urology, the First Affiliated Hospital with Nanjing Medical University, Nanjing, People's Republic of China.
Phys Med Biol. 2024 Feb 22;69(5). doi: 10.1088/1361-6560/ad25c7.
. To assist urologist and radiologist in the preoperative diagnosis of non-muscle-invasive bladder cancer (NMIBC) and muscle-invasive bladder cancer (MIBC), we proposed a combination models strategy (CMS) utilizing multiparametric magnetic resonance imaging.. The CMS includes three components: image registration, image segmentation, and multisequence feature fusion. To ensure spatial structure consistency of T2-weighted imaging (T2WI), diffusion-weighted imaging (DWI), and dynamic contrast-enhanced imaging (DCE), a registration network based on patch sampling normalized mutual information was proposed to register DWI and DCE to T2WI. Moreover, to remove redundant information around the bladder, we employed a segmentation network to obtain the bladder and tumor regions from T2WI. Using the coordinate mapping from T2WI, we extracted these regions from DWI and DCE and integrated them into a three-branch dual-channel input. Finally, to fully fuse low-level and high-level features of T2WI, DWI, and DCE, we proposed a distributed multilayer fusion model for preoperative MIBC prediction with five-fold cross-validation.. The study included 436 patients, of which 404 were for the internal cohort and 32 for external cohort. The MIBC was confirmed by pathological examination. In the internal cohort, the area under the curve, accuracy, sensitivity, and specificity achieved by our method were 0.928, 0.869, 0.753, and 0.929, respectively. For the urologist and radiologist, Vesical Imaging-Reporting and Data System score >3 was employed to determine MIBC. The urologist demonstrated an accuracy, sensitivity, and specificity of 0.842, 0.737, and 0.895, respectively, while the radiologist achieved 0.871, 0.803, and 0.906, respectively. In the external cohort, the accuracy of our method was 0.831, which was higher than that of the urologist (0.781) and the radiologist (0.813).. Our proposed method achieved better diagnostic performance than urologist and was comparable to senior radiologist. These results indicate that CMS can effectively assist junior urologists and radiologists in diagnosing preoperative MIBC.
为了帮助泌尿科医生和放射科医生术前诊断非肌肉浸润性膀胱癌（NMIBC）和肌肉浸润性膀胱癌（MIBC），我们提出了一种利用多参数磁共振成像的组合模型策略（CMS）。该 CMS 包括三个组成部分：图像配准、图像分割和多序列特征融合。为了确保 T2 加权成像（T2WI）、扩散加权成像（DWI）和动态对比增强成像（DCE）的空间结构一致性，我们提出了一种基于补丁采样归一化互信息的配准网络，用于将 DWI 和 DCE 配准到 T2WI。此外，为了去除膀胱周围的冗余信息，我们使用分割网络从 T2WI 中获取膀胱和肿瘤区域。利用 T2WI 的坐标映射，我们从 DWI 和 DCE 中提取这些区域，并将其集成到一个三分支双通道输入中。最后，为了充分融合 T2WI、DWI 和 DCE 的低层次和高层次特征，我们提出了一种分布式多层融合模型，用于进行术前 MIBC 预测，并进行了五折交叉验证。该研究纳入了 436 名患者，其中 404 名用于内部队列，32 名用于外部队列。MIBC 通过病理检查证实。在内部队列中，我们的方法的曲线下面积、准确率、敏感度和特异性分别为 0.928、0.869、0.753 和 0.929。对于泌尿科医生和放射科医生，Vesical Imaging-Reporting and Data System 评分＞3 用于确定 MIBC。泌尿科医生的准确率、敏感度和特异性分别为 0.842、0.737 和 0.895，而放射科医生的准确率、敏感度和特异性分别为 0.871、0.803 和 0.906。在外部队列中，我们的方法的准确率为 0.831，高于泌尿科医生（0.781）和放射科医生（0.813）。我们提出的方法的诊断性能优于泌尿科医生，与资深放射科医生相当。这些结果表明，CMS 可以有效地帮助初级泌尿科医生和放射科医生诊断术前 MIBC。
