基于多源双能CT图像的肿瘤内及瘤周放射组学用于术前预测膀胱癌的肌肉浸润情况

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

作者信息

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

机构信息

Department of Radiology, First Affiliated Hospital of Dalian Medical University, Dalian, China (M.H., J.Z., Q.C., W.W., Y.L. ).

CT Research, GE Healthcare, Dalian, China (J.L.).

出版信息

Acad Radiol. 2025 Jan;32(1):287-297. doi: 10.1016/j.acra.2024.08.010. Epub 2024 Aug 21.

DOI:10.1016/j.acra.2024.08.010

PMID:39168722

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.

摘要

目的

基于双能量CT尿路造影（DECTU）多图像评估肿瘤内及肿瘤周围的影像组学对膀胱癌（BCa）术前肌肉浸润状态的预测价值。

材料与方法

本回顾性分析纳入了202例行DECTU检查的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，以构建DECT模型。从静脉期的120 kVp类似图像、40 keV、100 keV和基于碘的物质分解（IMD）图像中提取肿瘤内及肿瘤周围向外3 mm区域的影像组学特征，并使用曼-惠特尼U检验、斯皮尔曼相关性分析和套索回归进行筛选。使用多层感知器为肿瘤内、肿瘤周围及肿瘤内和肿瘤周围（IntraPeri）区域建立影像组学模型。随后，通过整合多图像IntraPeri影像组学和DECT模型创建列线图。使用曲线下面积（AUC）、准确性、敏感性和特异性评估模型性能。

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，IntraPeri模型在40 keV图像（0.830对0.766对0.763）和IMD图像（0.881对0.840对0.821）中均表现出优于肿瘤内和肿瘤周围模型的性能。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），在预测BCa肌肉浸润状态方面优于DECT模型（AUC = 0.763，准确性 = 0.754，敏感性 = 0.632，特异性 = 0.810），差异具有统计学意义（p < 0.05）。