Integrating radiomics and real-world data to predict immune checkpoint inhibitor efficacy in advanced non-small-cell lung cancer

<h3>Background</h3> Immunotherapy (IO) revolutionized the prognosis of patients with non-small-cell lung cancer (NSCLC). However, identifying optimal candidates for this treatment remains challenging. Based on previous studies suggesting the potential power of radiomics in predicting clinical outcomes in different clinical settings, we aimed to assess its capability in predicting IO efficacy in advanced NSCLC patients. <h3>Materials and methods</h3> A total of 375 advanced NSCLC patients treated with IO-based regimens from April 2013 to May 2022 were enrolled. Primary lung lesions were segmented and radiomic features extracted. Using clinical benefit rate and overall survival status at 6 and 24 months (OS6 and OS24) as endpoints, machine learning classifiers were trained and then evaluated on a test set. <h3>Results</h3> Model achieving the highest performance predicting long-term survival (OS24) reached an accuracy of 0.71 and area under the curve of 0.79 on the test set, using the combination of radiomic features and real-world data (RWD) as input. Combining radiomics with RWD consistently allowed to outperform predictions obtained using the current standard predictive biomarker, i.e. programmed death-ligand 1 expression, for most of the outcomes. <h3>Conclusions</h3> We explored a radiomics-based signature with potential utility in predicting the prognosis of NSCLC patients undergoing IO. Further validation is required to confirm its clinical applicability and to support oncologists in making prognostic assessments.

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Publication Details

Journal
ESMO Real World Data and Digital Oncology
Published
2025-09-23
DOI
https://doi.org/10.1016/j.esmorw.2025.100182
Citations
1
Primary Topic
Radiomics and Machine Learning in Medical Imaging
Type
article
Field-Weighted Citation Impact
1.36

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article

Integrating radiomics and real-world data to predict immune checkpoint inhibitor efficacy in advanced non-small-cell lung cancer

A. Spagnoletti, F. de Braud, R. Vigorito, R. Di Mauro et al.
1 citations
ESMO Real World Data and Digital Oncology
Radiomics and Machine Learning in Medical Imaging
1.36
article

Integrating radiomics and real-world data to predict immune checkpoint inhibitor efficacy in advanced non-small-cell lung cancer

A. Spagnoletti, F. de Braud, R. Vigorito, R. Di Mauro, Margherita Ruggirello, Giuseppe Baselli, Marcello Restelli, V. Mišković, Andrea Riccardo Filippi, Mario Occhipinti, Giacomo Mazzoli, S. Sangaletti, Chiara Cavalli, Claudia Proto, Francesco Calimeri, S. Frasca, Bruno Marino, Rita Leporati, Francesco Trovò, Daniela Miliziano, L. Provenzano, L. Mazzeo, Valentina Bartolomeo, C. Agosta, Piergiorgio Solli, A.L.G. Pedrocchi, S. Manglaviti, Francesca Gabriella Greco, Rebecca Romanò, T. Beninato, Daniele Lorenzini, A. Vingiani, Alfonso Marchianò, M. Brambilla, G. Lo Russo, A.D. Dumitrascu, M. Ganzinelli, Elena De Momi, Carlotta Giani, G. Calareso, A. Ferrarin, Cecilia Silvestri, Alessandro Quarta, M. Monteleone, A. Zec, Anna Di Lello, Marina Chiara Garassino, G. Di Liberti, M. A. Favali, Arsela Prelaj, C. Bonalume, B. Guirges, M. Borraccino
article en
1 citations

Abstract

<h3>Background</h3> Immunotherapy (IO) revolutionized the prognosis of patients with non-small-cell lung cancer (NSCLC). However, identifying optimal candidates for this treatment remains challenging. Based on previous studies suggesting the potential power of radiomics in predicting clinical outcomes in different clinical settings, we aimed to assess its capability in predicting IO efficacy in advanced NSCLC patients. <h3>Materials and methods</h3> A total of 375 advanced NSCLC patients treated with IO-based regimens from April 2013 to May 2022 were enrolled. Primary lung lesions were segmented and radiomic features extracted. Using clinical benefit rate and overall survival status at 6 and 24 months (OS6 and OS24) as endpoints, machine learning classifiers were trained and then evaluated on a test set. <h3>Results</h3> Model achieving the highest performance predicting long-term survival (OS24) reached an accuracy of 0.71 and area under the curve of 0.79 on the test set, using the combination of radiomic features and real-world data (RWD) as input. Combining radiomics with RWD consistently allowed to outperform predictions obtained using the current standard predictive biomarker, i.e. programmed death-ligand 1 expression, for most of the outcomes. <h3>Conclusions</h3> We explored a radiomics-based signature with potential utility in predicting the prognosis of NSCLC patients undergoing IO. Further validation is required to confirm its clinical applicability and to support oncologists in making prognostic assessments.

ESMO Real World Data and Digital OncologyVol. 10
University of Milan (IT), University of Chicago Medical Center (US), Fondazione IRCCS Istituto Nazionale dei Tumori (IT), University of Calabria (IT), Sapienza University of Rome (IT), Politecnico di Milano (IT)
Ministero della Salute
Openalex Percentile: Top 16%
Radiomics and Machine Learning in Medical Imaging
1.36
DOIOpen Access PDFOpenAlex
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