Multi-DECT Image-based Intratumoral and Peritumoral Radiomics for Preoperative Prediction of Muscle Invasion in Bladder Cancer.

IF 3.8 2区医学 Q1 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING

Academic Radiology Pub Date : 2025-01-01 Epub Date: 2024-08-21 DOI:10.1016/j.acra.2024.08.010

Mengting Hu, Jingyi Zhang, Qiye Cheng, Wei Wei, Yijun Liu, Jianying Li, Lei Liu

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引用次数: 0

Abstract

Objectives: 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).

Material and methods: 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.

Results: 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).

Conclusion: The nomogram, incorporating IntraPeri radiomics and NIC, serves as a valuable and non-invasive tool for preoperatively assessing the muscle invasion status of BCa.

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基于多DECT图像的瘤内和瘤周放射组学用于术前预测膀胱癌的肌肉侵犯。

目的评估基于双能 CT 尿路造影（DECTU）多图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：这项回顾性分析涉及202名接受DECTU检查的膀胱癌患者。通过逐步回归分析构建 DECT 模型，将 DECTU 衍生的定量参数确定为风险因素。从静脉相 120 kVp-like、40 keV、100 keV 和基于碘的物质分解（IMD）图像中提取了瘤内和瘤外 3 mm 区域的放射组学特征，并使用 Mann-Whitney U 检验、Spearman 相关性分析和 LASSO 进行了筛选。使用多层感知器（Multilayer Perceptron）为瘤内、瘤周以及瘤内和瘤周（IntraPeri）区域建立了放射组学模型。随后，通过整合多图像 IntraPeri 放射组学和 DECT 模型，创建了一个提名图。使用曲线下面积（AUC）、准确性、灵敏度和特异性对模型性能进行了评估：结果：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，在 40 keV（0.830 vs. 0.766 vs. 0.763）和 IMD 图像（0.881 vs. 0.840 vs. 0.821）中，IntraPeri 模型的性能均优于瘤内和瘤周模型。在测试队列中，提名图显示出最佳预测能力（AUC=0.886，准确性=0.836，灵敏度=0.737，特异性=0.881），在预测 BCa 的肌肉侵犯状态方面优于 DECT 模型（AUC=0.763，准确性=0.754，灵敏度=0.632，特异性=0.810），差异有统计学意义（P 结论：在测试队列中，提名图显示出最佳预测能力（AUC=0.886，准确性=0.836，灵敏度=0.737，特异性=0.881），在预测 BCa 的肌肉侵犯状态方面优于 DECT 模型（AUC=0.763，准确性=0.754，灵敏度=0.632，特异性=0.810）：该提名图结合了 IntraPeri 放射组学和 NIC，是术前评估 BCa 肌肉侵犯状态的一种有价值的无创工具。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Academic Radiology 医学-核医学

CiteScore

7.60

自引率

10.40%

发文量

432

审稿时长

18 days

期刊介绍： Academic Radiology publishes original reports of clinical and laboratory investigations in diagnostic imaging, the diagnostic use of radioactive isotopes, computed tomography, positron emission tomography, magnetic resonance imaging, ultrasound, digital subtraction angiography, image-guided interventions and related techniques. It also includes brief technical reports describing original observations, techniques, and instrumental developments; state-of-the-art reports on clinical issues, new technology and other topics of current medical importance; meta-analyses; scientific studies and opinions on radiologic education; and letters to the Editor.