评估基于双能 CT 尿路造影 (DECTU) 多影像的瘤内和瘤周放射组学对术前预测膀胱癌 (BCa) 肌肉侵犯状态的预测价值。本回顾性分析涉及 202 名接受 DECTU 的 BCa 患者。通过逐步回归分析将DECTU衍生的定量参数确定为危险因素，构建DECT模型。从静脉相中的 120 kVp 类、40 keV、100 keV 和碘基材料分解 (IMD) 图像中提取瘤内和瘤周外 3 mm 区域的放射组学特征，并使用 Mann-Whitney 进行筛选U 检验、Spearman 相关分析和 LASSO。使用多层感知器针对肿瘤内、肿瘤周围以及肿瘤内和肿瘤周围 (IntraPeri) 区域开发放射组学模型。随后，通过集成多图像 IntraPeri 放射组学和 DECT 模型创建列线图。使用曲线下面积 (AUC)、准确性、敏感性和特异性来评估模型性能。归一化碘浓度 (NIC) 被确定为 DECT 模型的独立预测因子。在测试队列中，IntraPeri 模型在 40 keV（0.830 vs. 0.766 vs. 0.763）和 IMD 图像（0.881 vs. 0.840 vs. 0.821）方面均表现出优于瘤内和瘤周模型的性能。在测试队列中，列线图表现出最佳的预测能力（AUC=0.886，准确性=0.836，敏感性=0.737，特异性=0.881），优于DECT模型（AUC=0.763，准确性=0.754，敏感性=0.632，特异性= 0.810）在预测 BCa 肌肉侵袭状态方面具有统计学显着性差异（p < 0.05）。列线图结合了 IntraPeri 放射组学和 NIC，可作为术前评估 BCa 肌肉侵袭状态的有价值的非侵入性工具。版权所有 © 2024 年大学放射科医生协会。由爱思唯尔公司出版。保留所有权利。

To assess the predictive value of intratumoral and peritumoral radiomics based on Dual-energy CT urography (DECTU) multi-images for preoperatively predicting the muscle invasion status of bladder cancer (BCa).This retrospective analysis involved 202 BCa patients who underwent DECTU. DECTU-derived quantitative parameters were identified as risk factors through stepwise regression analysis to construct a DECT model. The radiomic features from the intratumoral and 3 mm outward peritumoral regions were extracted from the 120 kVp-like, 40 keV, 100 keV, and iodine-based material-decomposition (IMD) images in the venous-phase and were screened using Mann-Whitney U test, Spearman correlation analysis, and LASSO. Radiomics models were developed using the Multilayer Perceptron for the intratumoral, peritumoral and intra- and peritumoral (IntraPeri) regions. Subsequently, a nomogram was created by integrating the multi-image IntraPeri radiomics and DECT model. Model performance was evaluated using area-under-the-curve (AUC), accuracy, sensitivity, and specificity.Normalized iodine concentration (NIC) was identified as an independent predictor for the DECT model. The IntraPeri model demonstrated superior performance compared to the intratumoral and peritumoral models both in 40 keV (0.830 vs. 0.766 vs. 0.763) and IMD images (0.881 vs. 0.840 vs. 0.821) in the test cohort. In the test cohort, the nomogram exhibited the best predictability (AUC=0.886, accuracy=0.836, sensitivity=0.737, and specificity=0.881), outperformed the DECT model (AUC=0.763, accuracy=0.754, sensitivity=0.632, and specificity=0.810) in predicting muscle invasion status of BCa with a statistically significant difference (p < 0.05).The nomogram, incorporating IntraPeri radiomics and NIC, serves as a valuable and non-invasive tool for preoperatively assessing the muscle invasion status of BCa.Copyright © 2024 The Association of University Radiologists. Published by Elsevier Inc. All rights reserved.