Big Data

In a significant advancement for cancer treatment, researchers have developed a groundbreaking artificial intelligence (AI) tool capable of detecting lung tumors with exceptional accuracy—tracking their movement with every breath a patient takes. The new system, called iSeg, is the first 3D deep learning segmentation tool designed to map tumors as they shift during respiration, offering oncologists a powerful new asset in their mission to deliver precise and effective radiotherapy.

The use of artificial intelligence (AI) technologies and machine learning (ML) algorithms is experiencing tremendous growth in immunology, assisting in various functions such as identifying inflammatory markers related to different immune diseases and frailty, their potential application in designing next-generation monoclonal antibodies and vaccines, and their use to uncover complex patterns in human immune repertoires. The goal of this Research Topic is to highlight the key role of AI/ML in cancer immunotherapy. Given the wealth of existing immunotherapy data and its multi-modal, heterogeneous nature, AI can play a vital role in navigating through this complexity.

Tumor microenvironment signals and circulating factors may jointly influence treatment outcomes in patients with advanced renal cell carcinoma (RCC) who received nivolumab (Opdivo) plus cabozantinib (Cabometyx), according to David A Braun, MD, PhD. At the 2025 American Society of Clinical Oncology Annual Meeting, Braun, Center of Molecular and Cellular Oncology, Yale Cancer Center, Yale School of Medicine, discussed an integrative, post hoc analysis that used a machine learning-based approach to posthumously evaluate the phase 3 CheckMate 9ER trial (NCT03141177) to integrate both tumor and circulating biomarkers.

One of the fastest-growing parts of the economy in the last ten years has been healthcare, and in light of the growing threats of pandemics like the coronavirus outbreak, the industry is set to rise once again. To stay ahead of the curve in demand for healthcare services and solutions, organizations worldwide are turning to advanced techniques like AI, machine learning, and Big Data.

With the rapid advances in artificial intelligence-particularly convolutional neural networks-researchers now exploit CT, PET/CT and other imaging modalities to predict epidermal growth factor receptor (EGFR) mutation status in non-small-cell lung cancer (NSCLC) non-invasively, rapidly and repeatably. End-to-end deep-learning models simultaneously perform feature extraction and classification, capturing not only traditional radiomic signatures such as tumour density and texture but also peri-tumoural micro-environmental cues, thereby offering a higher theoretical performance ceiling than hand-crafted radiomics coupled with classical machine learning. Nevertheless, the need for large, well-annotated datasets, the domain shifts introduced by heterogeneous scanning protocols and preprocessing pipelines, and the "black-box" nature of neural networks all hinder clinical adoption. To address fragmented evidence and scarce external validation, we conducted a systematic review to appraise the true performance of deep-learning and radiomics models for EGFR prediction and to identify barriers to clinical translation, thereby establishing a baseline for forthcoming multicentre prospective studies.

At the 2025 ASCO Annual Meeting, artificial intelligence (AI) moved from theoretical promise to demonstrated clinical utility. Amid the surge of presentations on precision oncology, one theme became increasingly clear: as the number of actionable biomarkers increases, so does the complexity of sequencing treatments. AI may be the only tool agile enough to keep up. The biomarker pool is expanding rapidly. With tumor profiling now yielding dozens of potentially actionable signals, the sequencing question becomes central: Which target do we prioritize? What’s the optimal order of therapies? And how do we make decisions in the absence of head-to-head trials or long-term data?

The purpose of this research was to create and validate radiomic models based on machine learning that can effectively discriminate between primary non-small cell lung cancer (NSCLC) and small cell lung cancer (SCLC) in individuals with brain metastases (BMs) by utilizing high-dimensional radiomic characteristics derived from contrast-enhanced T1-weighted imaging (CE-T1WI).

A deep learning model was able to predict future lung cancer risk from a single low-dose chest CT scan, according to new research published at the ATS 2025 International Conference, and in American Journal of Respiratory and Critical Care Medicine. The model, called Sybil, which was originally developed using National Lung Screening Trial (NLST) data by investigators from the Massachusetts Institute of Technology and Harvard Medical School, could be used to guide more personalized lung cancer screening strategies.

María Jesús Ledesma, a researcher at UPM and CIBER-BBN, has developed an artificial intelligence algorithm capable of predicting which lung cancer patients will respond to immunotherapy. The tool is powered by real-world clinical data and routine blood tests, allowing it to predict treatment effectiveness and reduce adverse effects. The trailer, published in Cancer Immunology, Immunotherapy, represents a key step toward more accessible personalized medicine. The project was carried out in collaboration with leading hospitals and biomedical research centers in Spain.

Overdiagnosis in cancer care remains a significant concern, often resulting in unnecessary physical, emotional, and financial burdens on patients. Artificial intelligence (AI) has the potential to address this challenge by enabling more accurate, personalized cancer diagnoses and facilitating tailored treatment plans. Integrating AI with precision medicine can minimize unnecessary treatments and associated adverse effects by optimizing care strategies based on individual patient data. However, the integration of AI in oncology requires rigorous research and validation to ensure its effectiveness across diverse populations and clinical settings.

Según el Informe de EIT Health, aunque existen diferencias regionales, el sistema sanitario español en general ha alcanzado un alto nivel de madurez digital y se clasificó entre los cinco primeros de los 17 países de la Organización para la Cooperación y el Desarrollo Económicos (OCDE). Poseemos también sistemas sanitarios digitales altamente avanzados según el Índice de Salud Digital 2019 de la Fundación Bertelsmann.

La jefa del Laboratorio de Investigación Traslacional de la Fundación MD Anderson Cancer Center Madrid España, la doctora Gema Moreno-Bueno, ha afirmado que el Big Data y la Inteligencia Artificial (IA) permiten anticipar con una mayor precisión el comportamiento del cáncer.