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A CT-based Radiomics Signature Is Associated with Response to Immune Checkpoint Inhibitors in Advanced Solid Tumors
Abstract
Background Reliable predictive imaging markers of response to immune checkpoint inhibitors are needed. Purpose To develop and validate a pretreatment CT-based radiomics signature to predict response to immune checkpoint inhibitors in advanced solid tumors. Materials and Methods In this retrospective study, a radiomics signature was developed in patients with advanced solid tumors (including breast, cervix, gastrointestinal) treated with anti–programmed cell death–1 or programmed cell death ligand–1 monotherapy from August 2012 to May 2018 (cohort 1). This was tested in patients with bladder and lung cancer (cohorts 2 and 3). Radiomics variables were extracted from all metastases delineated at pretreatment CT and selected by using an elastic-net model. A regression model combined radiomics and clinical variables with response as the end point. Biologic validation of the radiomics score with RNA profiling of cytotoxic cells (cohort 4) was assessed with Mann-Whitney analysis. Results The radiomics signature was developed in 85 patients (cohort 1: mean age, 58 years ± 13 [standard deviation]; 43 men) and tested on 46 patients (cohort 2: mean age, 70 years ± 12; 37 men) and 47 patients (cohort 3: mean age, 64 years ± 11; 40 men). Biologic validation was performed in a further cohort of 20 patients (cohort 4: mean age, 60 years ± 13; 14 men). The radiomics signature was associated with clinical response to immune checkpoint inhibitors (area under the curve [AUC], 0.70; 95% CI: 0.64, 0.77; P < .001). In cohorts 2 and 3, the AUC was 0.67 (95% CI: 0.58, 0.76) and 0.67 (95% CI: 0.56, 0.77; P < .001), respectively. A radiomics-clinical signature (including baseline albumin level and lymphocyte count) improved on radiomics-only performance (AUC, 0.74 [95% CI: 0.63, 0.84; P < .001]; Akaike information criterion, 107.00 and 109.90, respectively). Conclusion A pretreatment CT-based radiomics signature is associated with response to immune checkpoint inhibitors, likely reflecting the tumor immunophenotype. © RSNA, 2021 Online supplemental material is available for this article. See also the editorial by Summers in this issue.
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“Radiology is one step ahead and shows what is possible with the emerging field of radiomics—the extraction of data from radiograms to diagnose cancer, predict outcomes or guide therapy (54–56).”
From Artificial Intelligence: Present and Future Potential for Solid Organ Transplantation (2022) · cites this paper as [55]
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Metadata
- Authors
- Ligero, M., Garcia-Ruiz, A., Viaplana, C., Villacampa, G., Raciti, M. V., Landa, J., Matos, I., Martin-Liberal, J., Ochoa-de-Olza, M., Hierro, C., Mateo, J., Gonzalez, M., Morales-Barrera, R., Suarez, C., Rodon, J., Elez, E., Braña, I., Muñoz-Couselo, E., Oaknin, A., Fasani, R., Nuciforo, P., Gil, D., Rubio-Perez, C., Seoane, J., Felip, E., Escobar, M., Tabernero, J., Carles, J., Dienstmann, R., Garralda, E., & Perez-Lopez, R.
- Affiliations
- Universitat Autònoma de Barcelona (ES)
- ORCID
- Ligero, Marta
- Garcia-Ruiz, Alonso
- Viaplana, Cristina
- Villacampa, Guillermo
- Raciti, Maria V.
- Matos, Ignacio
- Ochoa-de-Olza, Maria
- Hierro, Cinta
- Mateo, Joaquin
- Morales-Barrera, Rafael
- Suarez, Cristina
- Rodon, Jordi
- Braña, Irene
- Muñoz-Couselo, Eva
- Oaknin, Ana
- Fasani, Roberta
- Nuciforo, Paolo
- Gil, Debora
- Rubio-Perez, Carlota
- Seoane, Joan
- Felip, Enriqueta
- Escobar, Manuel
- Tabernero, Josep
- Carles, Joan
- Dienstmann, Rodrigo
- Garralda, Elena
- Perez-Lopez, Raquel
- Venue
- Radiology
- Published
- 2021-04
- Volume
- 299
- Issue
- 1
- Pages
- 109-119
- ISSN
- 0033-8419 (ISSN-L)
- 1527-1315
- OpenAlex
- W3124556679
- Cited by
- 107
- References
- 34
- Citations by year
- 2026: 9 · 2025: 19 · 2024: 22 · 2023: 26 · 2022: 18 · 2021: 10
- FWCI
- 10.04
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Radiology (publishedVersion)
- PubMed (publishedVersion)
- Funders
- Banco Bilbao Vizcaya Argentaria (RTI2018-095209-B-C21, FIS-G64384969)
- Generalitat de Catalunya (2017-SGR-1624)
- Topics
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Radiomics and Machine Learning in Medical Imaging
- Health Sciences › Medicine › Oncology › Cancer Immunotherapy and Biomarkers
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Medical Imaging Techniques and Applications
- Keywords
- radiomics signature, immune checkpoint inhibitors, predictive imaging biomarkers, treatment response prediction, advanced solid tumors, PD-1/PD-L1 inhibitors, metastasis, computed tomography, elastic net feature selection, albumin, lymphocyte count, bladder cancer, lung cancer, breast cancer, cervical cancer, gastrointestinal cancer, biomarker validation
- MeSH terms
- Immune Checkpoint Inhibitors, Aged, Female, Humans, Male, Middle Aged, Neoplasms, Retrospective Studies, Tomography, X-Ray Computed, Biomarkers, Tumor
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