Colon cancer remains one of the leading causes of cancer-related deaths worldwide—especially in patients with stage II and III disease, where the risk of relapse is difficult to predict with current tools.
In our latest study, published in ESMO Open, we developed a radiomics-based AI model that integrates clinical, radiomic, and fractal imaging biomarkers to predict relapse risk and time to recurrence in localized colon cancer.
The model significantly outperformed standard clinical methods, offering more accurate patient stratification through a novel “Risk Classification” score. As a non-invasive tool, it allows for effective assessment of relapse risk prior to surgery, helping physicians make therapeutic decisions and promoting personalized cancer care.
Prieto-de-la-Lastra, C., Carbonell-Asins, J. A., Bueno, A., Gómez-Alderete, A., Busto, M., Alcolado-Jaramillo, A. B., Jimenez-Pastor, A., Monzonís, X., Cuñat, A., Montagut, C., Moreno-Ruiz, P., Huerta, M., Roda, D., Gimeno-Valiente, F., Estepa-Fernández, A., Bellvís-Bataller, F., Fuster-Matanzo, A., Gibert, J., Roselló, S., Martinez-Ciarpaglini, C., Vidal, J., Alberich-Bayarri, Á., Cervantes, A., & Tarazona, N. (2025). A radiomics-based artificial intelligence model to assess the risk of relapse in localized colon cancer. ESMO Open, 10(8), 105495.
Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua. Ut enim ad minim veniam, quis nostrud exercitation ullamco laboris nisi ut aliquip ex ea commodo consequat.