Ana Fernández Montes, Lidia Nuñel Varela, Jacobo Pérez Abad, Ana Guedella López, Paula Sampedro Domarco,
Soledad Cameselle García, David Arias Ron, Renata Álvarez Llosa, Carme García Benito, Iria Tesouro Blanco,
María Regueiro Rodríguez

Complexo Hospitalario Universitario de Ourense. Ourensete

Antonio Calles Blanco
Servicio de Oncología. Hospital General Universitario Gregorio Marañón. Madrid

El software, denominado "Proteger", procesa los datos clínicos, comorbilidades y el nivel de fragilidad de pacientes de 65 años en adelante, con el objetivo de recomendar terapias personalizadas. La herramienta, en cuyo desarrollo participó un equipo del Centro de Oncología de Precisión UM, fue destacada en la prestigiosa revista Journal of Geriatric Oncology.

Machine learning (ML) is transforming cancer imaging by enhancing diagnosis, automating image analysis, and improving treatment planning. This review explores key ML applications, including tumor detection, radiomics, multi-modal imaging, and therapy monitoring. We discuss fundamental ML techniques, deep learning architectures, and data preprocessing strategies essential for medical imaging. ML-driven approaches have improved tumor segmentation, feature extraction, and computer-aided diagnosis across various cancer types. In cancer therapy, artificial intelligence (AI) aids radiotherapy planning, treatment response prediction, and real-time image-guided interventions. However, challenges such as data scarcity, model bias, and regulatory hurdles limit clinical adoption. Emerging solutions include explainable AI, federated learning for data privacy, and quantum computing for advanced imaging. Addressing these challenges through interdisciplinary collaboration will accelerate AI integration into clinical practice, enhancing cancer diagnosis and treatment.

Pablo Gajate Borau Hospital Universitario Ramón y Cajal. Madrid

Cancer patients are particularly susceptible to Drug–Drug Interactions (DDIs) due to frequent polypharmacy in oncology care. Co-administered drugs can increase toxicity or reduce effectiveness, potentially causing serious adverse events—for example, QTc-prolonging Tyrosine Kinase Inhibitors with CYP3A4 inhibitors can lead to torsade de pointes. Traditional DDI identification methods are time-consuming and costly, relying mainly on in vitro and in vivo wet lab experiments, clinical studies, or post-marketing surveillance. Many Machine Learning (ML) and Deep Learning (DL)-based DDI prediction models have been developed in recent decades to accelerate the identification of DDIs. We systematically reviewed ML- and DL-based DDI prediction models involving anticancer drugs. Key features of anticancer drugs involved and details of prediction models, such as the prediction tasks (existence or types of DDI) and performance, were summarised, as well as a list of newly predicted DDIs. Additionally, verification through up-to-date DrugBank and Drugs.com confirmed 22 of 96 newly predicted potential DDI drug pairs, demonstrating the practical value of these techniques. By understanding the current DDI prediction studies from both methodological and clinical standpoints, novel approaches may be tailored to the unique characteristics of oncology drugs, thereby enhancing the clinical relevance and applicability of DDI predictions.

Santiago Cabezas Camarero
Hospital Clínico San Carlos. Madrid

Fernando Moreno Antón, Alicia Escudero García, Beatriz González Fernández, Alfonso López de Sa
Servicio de Oncología Médica. Hospital Clínico San Carlos. Madrid

Almudena García Castaño
Servicio de Oncología Médica. Hospital Universitario Marqués de Valdecilla. Santander

Eva Muñoz-Couselo
Hospital Universitari Vall d’Hebron. Barcelona