Foundation Model and Radiomics-Based Quantitative Characterization of Perirenal Fat in Renal Cell Carcinoma Surgery.

Abstract

Rationale and objectives: To quantitatively characterize the degree of perirenal fat adhesion using artificial intelligence in renal cell carcinoma.

Materials and methods: This retrospective study analyzed a total of 596 patients from three cohorts, utilizing corticomedullary phase computed tomography urography (CTU) images. The nnUNet v2 network combined with numerical computation was employed to segment the perirenal fat region. Pyradiomics algorithms and a computed tomography foundation model were used to extract features from CTU images separately, creating single-modality predictive models for identifying perirenal fat adhesion. By concatenating the Pyradiomics and foundation model features, an early fusion multimodal predictive signature was developed. The prognostic performance of the single-modality and multimodality models was further validated in two independent cohorts.

Results: The nnUNet v2 segmentation model accurately segmented both kidneys. The neural network and thresholding approach effectively delineated the perirenal fat region. Single-modality models based on radiomic and computed tomography foundation features demonstrated a certain degree of accuracy in diagnosing and identifying perirenal fat adhesion, while the early feature fusion diagnostic model outperformed the single-modality models. Also, the perirenal fat adhesion score showed a positive correlation with surgical time and intraoperative blood loss.

Conclusion: AI-based radiomics and foundation models can accurately identify the degree of perirenal fat adhesion and have the potential to be used for surgical risk assessment.