Precision Oncology

Precision oncology aims to tailor cancer treatments to individual patients based on their tumor's unique characteristics, leveraging diverse data sources for improved diagnosis, prognosis, and therapy selection. Current research heavily utilizes deep learning, including convolutional neural networks (CNNs), transformers (like TopViTs), and multimodal learning frameworks that integrate genomic, imaging (e.g., whole-slide images, mammograms), and clinical data to build predictive models. These models are being developed and evaluated for tasks such as drug response prediction, target delineation for radiotherapy, and biomarker identification, with a strong emphasis on improving model reusability and generalizability across diverse datasets and institutions. The ultimate goal is to enhance the effectiveness and personalization of cancer care, leading to improved patient outcomes.