Cancer Response

Predicting cancer response to therapy is crucial for personalizing treatment and improving patient outcomes. Current research heavily utilizes machine learning, particularly deep learning models and support vector machines, analyzing diverse image data (2D and 3D) and incorporating clinical factors to predict response, often measured by metrics like RECIST scores. These models aim to automate and improve the accuracy of response assessment, potentially leading to more efficient and effective cancer treatment strategies. The integration of mechanistic and data-driven models is also a growing area of focus, aiming to create more comprehensive and predictive tools.