AI predictive modeling of survival outcomes for renal cancer patients undergoing targeted therapy

Abstract

Abstract Renal clear cell cancer (RCC) is a complex disease that is challenging to predict patient outcomes. Despite improvements with targeted therapy, personalized treatment planning is still needed. Artificial intelligence (AI) can help address this challenge by developing predictive models that accurately forecast patient survival periods. With AI-powered decision support, clinicians can provide patients with tailored treatment plans, enhancing treatment efficacy and quality of life. The study analyzed 267 patients with renal clear cell carcinoma, focusing on 26 who received targeted drug therapy. The data was refined by excluding 8 patients without enhanced CT scans. The research team categorized patients into two groups based on their expected lifespan: Group 1 (over 3 years) and Group 2 (under 3 years). The UPerNet algorithm was used to extract features from CT tumor markers, validating their effectiveness. These features were then used to develop an AI-based predictive model trained on the dataset. The developed AI model demonstrated remarkable accuracy, achieving a rate of 93.66% in Group 1 and 94.14% in Group 2. In conclusion, our study demonstrates the potential of AI technology in predicting the survival time of RCC patients undergoing targeted drug therapy. The established prediction model exhibits high predictive accuracy and stability, serving as a valuable tool for clinicians to facilitate the development of more personalized treatment plans for patients. This study highlights the importance of integrating AI technology in clinical decision-making, enabling patients to receive more effective and targeted treatment plans that enhance their overall quality of life.

Keywords

• Renal clear cell cancer
• Artificial intelligence
• Targeted therapy
• CT
• Imaging analysis