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| Main Authors: | , , , , , , |
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| Format: | Recurso digital |
| Language: | |
| Published: |
Zenodo
2025
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| Online Access: | https://doi.org/10.5281/zenodo.16728599 |
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Table of Contents:
- <p>Cancer is still a major global health concern, which is why individualized therapies that are based on each patient's unique genetic, environmental, and lifestyle characteristics are replacing generic ones. Enhancing therapeutic results, directing clinical judgment, and personalizing care are all made possible by predictive modelling. This interdisciplinary field models tumour growth, treatment responses, and disease progression by combining clinical insights, mathematics, and computational science. By combining a variety of factors, predictive modelling increases accuracy; it facilitates individualized treatment planning by combining genetic and tumour-specific data; and it helps with prognosis and drug development by using in silico simulations and early detection. Models of tumour growth can be empirical (Gompertz, Logistic), mechanistic (reaction-diffusion), or hybrid, and they are calibrated using data from genomics, multi-omics, and clinical imaging. Adaptive therapy techniques, real-time model updates, patient-specific parameter estimation, and the combination of pathomics and radiomics for thorough cancer analysis are some recent developments. Across all cancer types, deep learning improves early detection, classification, and diagnosis. Optimizing treatment plans with digital twins, anticipating immunotherapy and other therapy responses, comprehending drug resistance and tumour evolution, and facilitating virtual clinical trials are some of the key applications in personalized oncology.</p>