AI-HOPE lung cancer: a multicenter real-world registry integrating artificial intelligence for metastatic non-small-cell lung cancer
Year: 2026
Type: article
Study design: Observational Study
Abstract: Background The AI-HOPE Lung Cancer study is a multicenter initiative designed to integrate artificial intelligence (AI) and real-world data to improve outcome prediction in patients with metastatic non-small-cell lung cancer treated with first-line immunotherapy-based regimens. AI-HOPE aims to leverage machine learning (ML) models to generate individualized predictions of progression-free survival (PFS), overall survival (OS), and treatment-related toxicity in a broad, unselected population. Materials and methods Clinical and imaging data are harmonized and stored within a privacy-compliant infrastructure (San Raffaele Ai CEnter [S-RACE] platform), promoting FAIR (Findable, Accessible, Interoperable and Reusable) data principles and minimizing manual workload. The primary objective is the development of time-to-event models for PFS and OS. Complementary binary classification models will explore early progression, long-term survival, and clinically relevant toxicities. Results The study includes retrospective (from 2017) and prospective (until 2027) phases across 21 European centers. So far, 920 patients have been recruited for the study, of whom 621 have baseline imaging scans available for centralized analysis. In the AI-HOPE study, a flexible methodological approach integrates multiple ML models tailored to specific clinical questions, complemented by explainable AI tools. Multimodal models combining clinical variables with computed tomography and [ 18 F]2-fluoro-2-deoxy-d-glucose–positron emission tomography imaging features (when available) are supported through the S-RACE platform, which provides a partially automated imaging analysis workflow. Conclusions By combining structured clinical variables and multimodal imaging data, the AI-HOPE Lung Cancer study aims to support refined risk stratification and treatment personalization, ultimately facilitating the responsible integration of AI into routine thoracic oncology practice.
Authors: F.R. Ogliari, M. Ferrara, J. Huijs, A. Traverso, S. Barbieri, D. Tiano, P. Scuri, D. Celada, L. Papotto, L. Mollica, S. Oresti, R. Ferrara, M. Steens, C. Steendam, A. Merlini, A. Luciani, D. Lang, S. Grisanti, C. Genova, M. Gemelli, M. Flospergher, G. Finocchiaro, M.Y. El-Soud, D. Cortinovis, G. Cerea, F. Caspani, A. Brouns, F. Bensch, L. Belluomini, C. Bareggi, J. Albuquerque, A. Esposito, C. Tacchetti, A. Bulotta, L. Hendriks, M. Reni
Institutions: IRCCS Ospedale San Raffaele, Maastricht University Medical Centre, Maastricht University, Maastro Clinic, Vita-Salute San Raffaele University, VieCuri Medisch Centrum, Catharina Ziekenhuis, Ospedale San Luigi Gonzaga, University of Turin, Azienda Ospedaliera Ospedale Civile di Legnano, Johannes Kepler University of Linz, Kepler Universitätsklinikum, Azienda Socio Sanitaria Territoriale degli Spedali Civili di Brescia, Ospedale Policlinico San Martino, MultiMedica, Ospedale di Circolo e Fondazione Macchi, IRCCS Humanitas Research Hospital, Máxima Medisch Centrum, University of Milano-Bicocca, Azienda Socio Sanitaria Territoriale Grande Ospedale Metropolitano Niguarda, Azienda Socio Sanitaria Territoriale Lariana, Zuyderland Medisch Centrum, University Medical Center Groningen, University of Groningen, University of Verona, Azienda Ospedaliera Universitaria Integrata Verona, Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, Hospital da Luz
Language: English
Subfield: Radiology, Nuclear Medicine and Imaging
Field: Medicine
Domain: Health Sciences
Open Access status: gold
Estimated APC paid: $3,170
OpenAlex ID: W7172284079