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

被引:0
|
作者
Hu, Mengting [1 ]
Zhang, Jingyi [1 ]
Cheng, Qiye [1 ]
Wei, Wei [1 ]
Liu, Yijun [1 ]
Li, Jianying [2 ]
Liu, Lei [3 ]
机构
[1] Dalian Med Univ, Affiliated Hosp 1, Dept Radiol, Dalian, Peoples R China
[2] GE Healthcare, CT Res, Dalian, Peoples R China
[3] Dalian Med Univ, Affiliated Hosp 1, Dept Urol, Dalian, Peoples R China
关键词
Non-Muscle Invasive Bladder Neoplasms; Radiomics; Tomography; X-Ray Computed; Artificial Intelligence;
D O I
10.1016/j.acra.2024.08.010
中图分类号
R8 [特种医学]; R445 [影像诊断学];
学科分类号
1002 ; 100207 ; 1009 ;
摘要
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.
引用
收藏
页码:287 / 297
页数:11
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