Treatment Response Prediction

Treatment response prediction aims to forecast how individual patients will react to specific cancer therapies, improving treatment selection and patient outcomes. Current research heavily utilizes machine learning, employing various architectures such as deep learning models (including convolutional neural networks and transformers), kernel methods, and graph learning approaches to analyze diverse data types, including whole slide images, multi-modal clinical data, and genomic information. These advancements are improving the accuracy of predictions across different cancer types and treatment modalities, ultimately leading to more personalized and effective cancer care. The field's focus is on robust model development, handling incomplete data, and providing reliable uncertainty quantification to enhance clinical decision-making.