Free Survival

Free survival prediction in oncology focuses on developing models to estimate the time until disease recurrence or progression, aiding in personalized treatment strategies and risk stratification. Current research heavily utilizes machine learning, particularly deep learning architectures like convolutional neural networks and graph neural networks, often incorporating multimodal data (e.g., PET/CT images, clinical variables) to improve prediction accuracy. These advancements leverage radiomics features and automated image analysis to reduce reliance on subjective manual segmentation, enhancing reproducibility and potentially improving the accuracy of risk assessment compared to clinical data alone. Improved prediction models can lead to more effective treatment planning and improved patient outcomes.