Image-Based Prognostics: Predicting Disease and Personalizing Care
*Corresponding Author: Dr. Tomas Rojas, Department of Prognostic Imaging, National University of Asunci贸n, Paraguay, Email: t.rojas@progimg.pyReceived Date: Nov 03, 2025 / Published Date: Nov 28, 2025
Citation: Rojas DT (2025) Image-Based Prognostics: Predicting Disease and Personalizing Care. J Radiol 14: 752.DOI: 10.4172/2167-7964.1000752
Copyright: © 2025 Dr. Tomas Rojas This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution and reproduction in any medium, provided the original author and source are credited.
Abstract
Image-based prognostics utilize advanced imaging and artificial intelligence to predict disease trajectories, moving beyond diagnosis to personalized patient management. Radiomics and deep learning extract quantitative features for prognostic modeling. Integration with clinical and genomic data enhances predictive power. Robust validation, ethical data handling, and collaborative clinical translation are essential. Quantitative biomarkers from standard imaging offer cost-effective prognostic insights across oncology. Future advancements focus on dynamic biomarkers and longitudinal analysis for improved outcome prediction and treatment personalization.
