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Abstract Background: Renal clear cell cancer (RCC) is a complex and heterogeneous disease, posing significant challenges in predicting patient outcomes. The introduction of targeted drug therapy has improved treatment outcomes, but there is still a pressing need for personalized and effective treatment planning. Artificial intelligence (AI) has emerged as a promising tool in addressing this challenge, enabling the development of predictive models that can accurately forecast patient survival periods. By harnessing the power of AI, clinicians can be empowered with decision support, enabling patients to receive more tailored treatment plans that enhance both treatment efficacy and quality of life. Methods: To achieve this goal, we conducted a retrospective analysis of clinical data from The Cancer Imaging Archive (TCIA) and categorized RCC patients receiving targeted therapy into two groups: Group 1 (anticipated lifespan exceeding 3 years) and Group 2 (anticipated lifespan of less than 3 years). We utilized the UPerNet algorithm to extract pertinent features from CT markers of tumors and validate their efficacy. The extracted features were then used to develop an AI-based predictive model that was trained on the dataset. Results: The developed AI model demonstrated remarkable accuracy, achieving a rate of 93.66% in Group 1 and 94.14% in Group 2. Conclusions: 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.
Yu et al. (Wed,) studied this question.