AI-HOPE lung cancer: a multicenter real-world registry integrating artificial intelligence for metastatic non-small-cell lung cancer

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.

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Journal
ESMO Real World Data and Digital Oncology
Published
2026-08-03
DOI
https://doi.org/10.1016/j.esmorw.2026.100736
Primary Topic
Radiomics and Machine Learning in Medical Imaging
Type
article
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article

AI-HOPE lung cancer: a multicenter real-world registry integrating artificial intelligence for metastatic non-small-cell lung cancer

C. Steendam, Lizza Hendriks, A. Bulotta, Andrea Luciani et al.
ESMO Real World Data and Digital Oncology
Radiomics and Machine Learning in Medical Imaging
article

AI-HOPE lung cancer: a multicenter real-world registry integrating artificial intelligence for metastatic non-small-cell lung cancer

C. Steendam, Lizza Hendriks, A. Bulotta, Andrea Luciani, M. Steens, Giovanna Finocchiaro, Joana Albuquerque, Carlo Tacchetti, Alessandra Merlini, F. Caspani, Francesca Rita Ogliari, Simone Barbieri, R. Ferrara, Diego Cortinovis, Donato Tiano, Frederike Bensch, Lorenzo Belluomini, M. Ferrara, C. Bareggi, Carlo Genova, S. Oresti, Maria Gemelli, David Lang, Jarno W J Huijs, M. Reni, A. Traverso, A. Esposito, L. Mollica, A. Brouns, P. Scuri, G. Cerea, L. Papotto, M. Flospergher, M.Y. El-Soud, S. Grisanti, D. Celada
article en

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.

ESMO Real World Data and Digital OncologyVol. 13
University of Verona (IT), Johannes Kepler University of Linz (AT), Radboud University Nijmegen (NL), Vita-Salute San Raffaele University (IT), Maastricht University Medical Centre (NL), Catharina Ziekenhuis (NL), Ospedale di Circolo e Fondazione Macchi (IT), Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico (IT), Maastricht University (NL), Azienda Socio Sanitaria Territoriale Grande Ospedale Metropolitano Niguarda (IT), MultiMedica (IT), Azienda Socio Sanitaria Territoriale degli Spedali Civili di Brescia (IT), Dialyse Centrum Groningen (NL), VieCuri Medisch Centrum (NL), Máxima Medisch Centrum (NL), Azienda Socio Sanitaria Territoriale Lariana (IT), Hospital da Luz (PT), IRCCS Ospedale San Raffaele (IT), Zuyderland Medisch Centrum (NL), Ospedale Policlinico San Martino (IT), Istituti di Ricovero e Cura a Carattere Scientifico (IT), Ospedale San Luigi Gonzaga (IT), Maastro Clinic (NL), Azienda Ospedaliera Ospedale Civile di Legnano (IT), IRCCS Humanitas Research Hospital (IT), Istituto di Ricovero e Cura a Carattere Scientifico San Raffaele (IT), University of Turin (IT), University of Milano-Bicocca (IT)
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