Recurrent Renal Cell Cancer

This is a prospective, single-arm study in adults with recurrent or metastatic clear cell renal cell carcinoma (ccRCC). Participants will receive standard systemic therapy (targeted therapy plus anti-PD-1 immunotherapy) and undergo dual PET/CT imaging (FDG PET/CT and a CAIX-targeted PET/CT) to map disease sites. When feasible, PET/CT-visible lesions will be treated with image-guided stereotactic ablative radiotherapy (SABR). Patients will be followed to evaluate progression-free survival, local control of treated lesions, and treatment-related adverse events (planned enrollment: \~70).

Renal cancer ranks seventh in incidence among men and sixth among women in the Beijing area, with Peking University First Hospital treating over 1,000 kidney cancer patients annually. Once recurrence or metastasis occurs, the prognosis is poor, with median progression times of 1-2 years after first-line systemic therapy (targeted therapy combined with immunotherapy). Enhancing local control of lesions is key to improving overall survival. Combining local radiotherapy with systemic treatment may be one approach to address this issue. Currently, Stereotactic Ablative Radiotherapy (SABR) enables precise tumor ablation and can activate the body's immune response. Studies show that the one-year local control rate after SABR exceeds 90%. Preliminary research by the applicant has shown that the combination of drug therapy and SABR for recurrent metastatic renal cancer can extend progression-free survival beyond two years, with earlier intervention leading to more significant survival improvements. This study aims to evaluate the efficacy and safety of combining SABR with targeted and immunotherapy for recurrent metastatic renal cancer through a multicenter, bidirectional cohort design, exploring new therapeutic strategies.

This study aims to preoperatively predict DFS of patients with localised ccRCC using a deep learning prognostic model based on enhanced contrast CT images, validate it's predictive ability in multicentre data and compare it's predictive ability with traditional models.