Background
Renal cell carcinoma (RCC) is often detected incidentally, and a notable proportion of patients present with stage IV disease at diagnosis when therapeutic options are more limited and prognosis is poorer1.
Earlier identification of RCC remains a major unmet need, with the potential to improve survival and reduce treatment intensity.
AI models are increasingly being developed to analyze abdominal CT imaging and uncover subtle radiologic patterns associated with early-stage RCC, supporting earlier diagnosis and more effective intervention2.
Objective
A top-five global pharmaceutical company contacted Aigora to obtain a regulatory-grade, longitudinal real-world dataset to power AI solutions for early RCC detection.
Delivery
Aigora identified patients across its network with histopathology-confirmed RCC and abdominal CT imaging acquired in the years preceding diagnosis, and assembled a multimodal dataset enriched with comprehensive clinical data and pathology data.
