AI-HOPE lung cancer: a multicenter real-world registry integrating artificial intelligence for metastatic non-small-cell lung cancer.
Authors
Affiliations (24)
Affiliations (24)
- Department of Oncology, IRCCS Ospedale San Raffaele, Milan, Italy.
- Department of Respiratory Medicine, Maastricht University Medical Center, GROW - Research Institute for Oncology and Reproduction, Maastricht, The Netherlands.
- Department of Radiation Oncology (Maastro Clinic), Maastricht University Medical Center, GROW - Research Institute for Oncology and Reproduction, Maastricht, The Netherlands.
- University Vita-Salute San Raffaele, Milan, Italy.
- Department of Pulmonary Diseases, VieCuri Hospital, Venlo, The Netherlands.
- Department of Pulmonary Diseases, Catharina Hospital, Eindhoven, The Netherlands.
- Department of Oncology, University of Turin, AOU San Luigi Gonzaga, Orbassano, Italy.
- Department of Oncology, Ospedale di Legnano, Italy.
- Department of Pulmonology, Kepler University Hospital, Johannes Kepler University, Linz, Austria.
- Department of Oncology, Spedali Civili di Brescia, Italy.
- Department of Oncology, IRCCS Ospedale Policlinico San Martino, Genova, Italy.
- Department of Oncology, IRCCS Multimedica Sesto San Giovanni, Italy.
- Department of Oncology, Ospedale di Circolo e Fondazione Macchi, Varese, Italy.
- Department of Oncology, IRCCS Humanitas Research Hospital, Rozzano, Milan, Italy.
- Department of Pulmonology, Maxima MC, Veldhoven, The Netherlands.
- Medical Oncology, Fondazione IRCCS San Gerardo dei Tintori, Monza; Department of Medicine Università Milano-Bicocca, Milan, Italy.
- Department of Oncology, ASST Grande Ospedale Metropolitano Niguarda, Milan, Italy.
- Department of Oncology, ASST Lariana, Como, Italy.
- Department of Pulmonology, Zuyderland MC, Heerlen, The Netherlands.
- Department of Pulmonology, UMC Groningen, The Netherlands.
- Department of Engineering for Innovation Medicine (DIMI), University of Verona School of Medicine and Verona University Hospital Trust, Verona, Italy.
- Department of Oncology, Fondazione IRCCS Ca' Granda-Ospedale Maggiore Policlinico, Milan, Italy.
- Department of Oncology, Hospital da Luz, Lisboa, Portugal.
- Experimental Imaging Center, IRCCS San Raffaele Hospital, Milan, Italy.
Abstract
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. 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. 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 [<sup>18</sup>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. 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.