Interplay between MRI radiomics and immune gene expression signatures in oral squamous cell carcinoma

With the advances in immunotherapy and the challenge of poor responsiveness in oral squamous cell carcinoma (OSCC) patients, understanding the tumor microenvironment is crucial. Radiogenomics offers the potential to provide pre-operative, non-invasive image-derived immune biomarkers. To this aim, the present study explores the capability of MRI-based radiomics to describe patients' immune state in OSCC. Seven MRI-based radiomic, 29 immune-related gene expression signatures were computed and deconvolution analysis was performed for a subset of OSCC from the BD2Decide database. A correlation-driven analysis identified key associations between radiomic and immune-related signatures and cell populations. Radiomic classifiers of the gene expression signatures were then developed to evaluate their capability to stratify patients based on immune status. MRI-based radiomic models showed promising results in predicting a gene expression signature associated with significant prognostic value for HNSCC patients who underwent radiotherapy (AUC = 0.92), suggesting these models' potential in distinguishing radioresistant from radiosensitive patients, aiding treatment decisions. Additionally, radiomic signatures reflected immune infiltrating cells in our cohort (M1, CD8 + T, B cells). MRI-radiomic signatures and associated models could become non-invasive methods to evaluate the prognosis and treatment choice in OSCC patients. Based on our promising results, and upon external validation, MRI-radiomics could enhance personalized medicine approaches.

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Publication Details

Journal
Scientific Reports
Published
2025-04-12
DOI
https://doi.org/10.1038/s41598-025-96821-x
Citations
7
Primary Topic
Radiomics and Machine Learning in Medical Imaging
Type
article
Field-Weighted Citation Impact
9.52

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article

Interplay between MRI radiomics and immune gene expression signatures in oral squamous cell carcinoma

Anna Corti, Marco Ravanelli, Tito Poli, Davide Mattavelli et al.
7 citations
Scientific Reports
Radiomics and Machine Learning in Medical Imaging
9.52
article

Interplay between MRI radiomics and immune gene expression signatures in oral squamous cell carcinoma

Anna Corti, Marco Ravanelli, Tito Poli, Davide Mattavelli, Lisa Francesca Licitra, Loris De Cecco, Luca Mainardi, Stefano Cavalieri, Valentina Corino, Deborah Lenoci
article en
7 citations

Abstract

With the advances in immunotherapy and the challenge of poor responsiveness in oral squamous cell carcinoma (OSCC) patients, understanding the tumor microenvironment is crucial. Radiogenomics offers the potential to provide pre-operative, non-invasive image-derived immune biomarkers. To this aim, the present study explores the capability of MRI-based radiomics to describe patients' immune state in OSCC. Seven MRI-based radiomic, 29 immune-related gene expression signatures were computed and deconvolution analysis was performed for a subset of OSCC from the BD2Decide database. A correlation-driven analysis identified key associations between radiomic and immune-related signatures and cell populations. Radiomic classifiers of the gene expression signatures were then developed to evaluate their capability to stratify patients based on immune status. MRI-based radiomic models showed promising results in predicting a gene expression signature associated with significant prognostic value for HNSCC patients who underwent radiotherapy (AUC = 0.92), suggesting these models' potential in distinguishing radioresistant from radiosensitive patients, aiding treatment decisions. Additionally, radiomic signatures reflected immune infiltrating cells in our cohort (M1, CD8 + T, B cells). MRI-radiomic signatures and associated models could become non-invasive methods to evaluate the prognosis and treatment choice in OSCC patients. Based on our promising results, and upon external validation, MRI-radiomics could enhance personalized medicine approaches.

Scientific ReportsVol. 15(1)
University of Parma (IT), University of Milan (IT), Centro Cardiologico Monzino (IT), Fondazione IRCCS Istituto Nazionale dei Tumori (IT), University of Brescia (IT), Politecnico di Milano (IT)
Associazione Italiana per la Ricerca sul Cancro
Openalex Percentile: Top 2%
Radiomics and Machine Learning in Medical Imaging
9.52
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