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Published on: March 30, 2015
CT-based AI multidimensional volumetric parameters for predicting surgical complexity and T stage in renal cell
Shengfa Lin1, Liqing Su2, Yuhan Huang3
1Department of Radiology, Shengli Clinical Medical College of Fujian Medical University, Fujian Provincial Hospital, Fuzhou University Affiliated Provincial Hospital, Fuzhou, Fujian 350001, China; Department of Oncology and Vascular Intervention, Clinical Oncology School of Fujian Medical University, Fujian Cancer Hospital, Fuzhou, Fujian 350011,China.
Background:
Based on contrast-enhanced computed tomography (CT) arterial phase images of renal cell carcinoma (RCC), this study aimed to develop an automatic segmentation model for kidneys and RCC, and automatically extract tumor multidimensional volumetric parameters to predict the complexity of nephron-sparing surgery (NSS) and pathological T stage.
Materials And Methods:
Arterial phase CT images of 600 RCC patients who underwent NSS were retrospectively collected from two medical centers. A high-quality annotated dataset was constructed using the "full manual fine annotation - semi-automatic rough annotation - manual review" strategy, and a kidney and tumor segmentation model was trained based on the nnU-Net framework. Multidimensional volumetric parameters, including total tumor volume (VT), endophytic volume (VE), and endophytic ratio (VE/VT), were extracted. Model performance was evaluated using Dice Similarity Coefficient (DSC), Normalized Surface Distance (NSD), and 95th percentile Hausdorff Distance (HD95). Intraclass Correlation Coefficient (ICC) was used to analyze the consistency between AI-derived multidimensional volumetric parameters and manual gold standard. Kruskal-Wallis test and Dunn's multiple comparisons were applied to assess the predictive ability of these parameters for surgical complexity (warm ischemia time [WIT], surgical duration, intraoperative blood loss) and T stage.
Results:
The DSC of the segmentation model for kidneys and tumors was 0.94 and 0.81, respectively; NSD was 0.93 and 0.89, respectively; HD95 was 9.66 and 11.97, respectively. AI-derived multidimensional volumetric parameters showed excellent consistency with the manual gold standard (ICC: 0.942-0.986, all P < 0.001). Stratified analysis revealed that AI-VT and AI-VE could effectively stratify and predict surgical complexity and T stage (all P < 0.05). Although AI-VE/VT lacked significant stratification ability, it could serve as an objective supplementary parameter for preoperative scoring systems.
Conclusion:
The AI segmentation model constructed in this study can accurately achieve automatic segmentation of kidneys and RCC. The AI multidimensional volumetric parameters can effectively predict NSS complexity and T stage, providing a reliable quantitative tool for preoperative individualized assessment and surgical planning of RCC patients with good clinical translation potential.