Radiomics and Delta-Radiomics Signatures to Predict Response and Survival in Patients with Non-Small-Cell Lung Cancer Treated with Immune Checkpoint Inhibitors
The aim of our study was to determine the potential role of CT-based radiomics in predicting treatment response and survival in patients with advanced NSCLC treated with immune checkpoint inhibitors. We retrospectively included 188 patients with NSCLC treated with PD-1/PD-L1 inhibitors from two independent centers. Radiomics analysis was performed on pre-treatment contrast-enhanced CT. A delta-radiomics analysis was also conducted on a subset of 160 patients who underwent a follow-up contrast-enhanced CT after 2 to 4 treatment cycles. Linear and random forest (RF) models were tested to predict response at 6 months and overall survival. Models based on clinical parameters only and combined clinical and radiomics models were also tested and compared to the radiomics and delta-radiomics models. The RF delta-radiomics model showed the best performance for response prediction with an AUC of 0.8 (95% CI: 0.65-0.95) on the external test dataset. The Cox regression delta-radiomics model was the most accurate at predicting survival with a concordance index of 0.68 (95% CI: 0.56-0.80) (p = 0.02). The baseline CT radiomics signatures did not show any significant results for treatment response prediction or survival. In conclusion, our results demonstrated the ability of a CT-based delta-radiomics signature to identify early on patients with NSCLC who were more likely to benefit from immunotherapy.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2023 |
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| Erschienen: |
2023 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:15 |
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| Enthalten in: |
Cancers - 15(2023), 7 vom: 25. März |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Cousin, François [VerfasserIn] |
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| Links: |
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| Themen: |
Computed tomography |
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| Anmerkungen: |
Date Revised 15.04.2023 published: Electronic Citation Status PubMed-not-MEDLINE |
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| doi: |
10.3390/cancers15071968 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| PPN (Katalog-ID): |
NLM355509873 |
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| 500 | |a Citation Status PubMed-not-MEDLINE | ||
| 520 | |a The aim of our study was to determine the potential role of CT-based radiomics in predicting treatment response and survival in patients with advanced NSCLC treated with immune checkpoint inhibitors. We retrospectively included 188 patients with NSCLC treated with PD-1/PD-L1 inhibitors from two independent centers. Radiomics analysis was performed on pre-treatment contrast-enhanced CT. A delta-radiomics analysis was also conducted on a subset of 160 patients who underwent a follow-up contrast-enhanced CT after 2 to 4 treatment cycles. Linear and random forest (RF) models were tested to predict response at 6 months and overall survival. Models based on clinical parameters only and combined clinical and radiomics models were also tested and compared to the radiomics and delta-radiomics models. The RF delta-radiomics model showed the best performance for response prediction with an AUC of 0.8 (95% CI: 0.65-0.95) on the external test dataset. The Cox regression delta-radiomics model was the most accurate at predicting survival with a concordance index of 0.68 (95% CI: 0.56-0.80) (p = 0.02). The baseline CT radiomics signatures did not show any significant results for treatment response prediction or survival. In conclusion, our results demonstrated the ability of a CT-based delta-radiomics signature to identify early on patients with NSCLC who were more likely to benefit from immunotherapy | ||
| 650 | 4 | |a Journal Article | |
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| 700 | 1 | |a Louis, Thomas |e verfasserin |4 aut | |
| 700 | 1 | |a Dheur, Sophie |e verfasserin |4 aut | |
| 700 | 1 | |a Aboubakar, Frank |e verfasserin |4 aut | |
| 700 | 1 | |a Ghaye, Benoit |e verfasserin |4 aut | |
| 700 | 1 | |a Occhipinti, Mariaelena |e verfasserin |4 aut | |
| 700 | 1 | |a Vos, Wim |e verfasserin |4 aut | |
| 700 | 1 | |a Bottari, Fabio |e verfasserin |4 aut | |
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| 700 | 1 | |a Duysinx, Bernard |e verfasserin |4 aut | |
| 700 | 1 | |a Guiot, Julien |e verfasserin |4 aut | |
| 700 | 1 | |a Hustinx, Roland |e verfasserin |4 aut | |
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