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

Patients diagnosed with breast cancer exhibit a diverse range of prognostic outcomes due to the varied nature of the disease across different patient groups. To address this complexity and enhance prognostic predictions based on gene expression data from breast cancer samples, this study has developed an integrated deep learning method that combines Convolutional Neural Networks (CNN) with Bidirectional Long Short-Term Memory (BiLSTM) networks.

Santiago Cabezas Camarero
Hospital Clínico San Carlos. Madrid

Elena Aguirre Ortega
Hospital Quirón Salud. Zaragoza

Begoña Bermejo de las Heras
Hospital Clínico Universitario de Valencia. València

Gastric cancer (GC) remains a global clinical challenge due to late diagnosis, high heterogeneity, and poor prognosis. Tumor stemness has emerged as a key factor driving tumor aggressiveness and therapeutic resistance. However, the systematic characterization of high-stemness GC cells and their molecular features remains limited. We integrated single-cell RNA sequencing (scRNA-seq), spatial transcriptomics, and bulk RNA-seq data to identify and characterize high-stemness GC cells.

Raúl Márquez Vázquez
MD Anderson Cancer Center Madrid. Madrid

Webinar “Controversias y decisiones en cáncer de ovario avanzado (COA): de la última evidencia en cirugía a la implicación clínica” de la Alianza AstraZeneca-MSD.

Artificial intelligence (AI) is increasingly being integrated across the cancer care continuum, from early detection and diagnosis to treatment selection and clinical trial design. Standardized metrics are needed to ensure adequate, reliable machine learning model performance and clinical relevance across diverse settings. Becoming fluent in the language of AI will empower oncologists to participate in shaping the role of new technologies in oncology research and practice.