A radiomics signature derived from CT imaging to predict MSI status and immunotherapy outcomes in gastric cancer : a multi-cohort study
© 2024. The Author(s)..
BACKGROUND: Accurate microsatellite instability (MSI) testing is essential for identifying gastric cancer (GC) patients eligible for immunotherapy. We aimed to develop and validate a CT-based radiomics signature to predict MSI and immunotherapy outcomes in GC.
METHODS: This retrospective multicohort study included a total of 457 GC patients from two independent medical centers in China and The Cancer Imaging Archive (TCIA) databases. The primary cohort (n = 201, center 1, 2017-2022), was used for signature development via Least Absolute Shrinkage and Selection Operator (LASSO) and logistic regression analysis. Two independent immunotherapy cohorts, one from center 1 (n = 184, 2018-2021) and another from center 2 (n = 43, 2020-2021), were utilized to assess the signature's association with immunotherapy response and survival. Diagnostic efficiency was evaluated using the area under the receiver operating characteristic curve (AUC), and survival outcomes were analyzed via the Kaplan-Meier method. The TCIA cohort (n = 29) was included to evaluate the immune infiltration landscape of the radiomics signature subgroups using both CT images and mRNA sequencing data.
RESULTS: Nine radiomics features were identified for signature development, exhibiting excellent discriminative performance in both the training (AUC: 0.851, 95%CI: 0.782, 0.919) and validation cohorts (AUC: 0.816, 95%CI: 0.706, 0.926). The radscore, calculated using the signature, demonstrated strong predictive abilities for objective response in immunotherapy cohorts (AUC: 0.734, 95%CI: 0.662, 0.806; AUC: 0.724, 95%CI: 0.572, 0.877). Additionally, the radscore showed a significant association with PFS and OS, with GC patients with a low radscore experiencing a significant survival benefit from immunotherapy. Immune infiltration analysis revealed significantly higher levels of CD8 + T cells, activated CD4 + B cells, and TNFRSF18 expression in the low radscore group, while the high radscore group exhibited higher levels of T cells regulatory and HHLA2 expression.
CONCLUSION: This study developed a robust radiomics signature with the potential to serve as a non-invasive biomarker for GC's MSI status and immunotherapy response, demonstrating notable links to post-immunotherapy PFS and OS. Additionally, distinct immune profiles were observed between low and high radscore groups, highlighting their potential clinical implications.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2024 |
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| Erschienen: |
2024 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:24 |
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| Enthalten in: |
BMC cancer - 24(2024), 1 vom: 01. Apr., Seite 404 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Zhan, Peng-Chao [VerfasserIn] |
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| Links: |
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| Themen: |
Gastric cancer |
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| Anmerkungen: |
Date Completed 03.04.2024 Date Revised 04.04.2024 published: Electronic Citation Status MEDLINE |
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| doi: |
10.1186/s12885-024-12174-0 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| PPN (Katalog-ID): |
NLM37051145X |
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| 245 | 1 | 2 | |a A radiomics signature derived from CT imaging to predict MSI status and immunotherapy outcomes in gastric cancer |b a multi-cohort study |
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| 500 | |a Citation Status MEDLINE | ||
| 520 | |a © 2024. The Author(s). | ||
| 520 | |a BACKGROUND: Accurate microsatellite instability (MSI) testing is essential for identifying gastric cancer (GC) patients eligible for immunotherapy. We aimed to develop and validate a CT-based radiomics signature to predict MSI and immunotherapy outcomes in GC | ||
| 520 | |a METHODS: This retrospective multicohort study included a total of 457 GC patients from two independent medical centers in China and The Cancer Imaging Archive (TCIA) databases. The primary cohort (n = 201, center 1, 2017-2022), was used for signature development via Least Absolute Shrinkage and Selection Operator (LASSO) and logistic regression analysis. Two independent immunotherapy cohorts, one from center 1 (n = 184, 2018-2021) and another from center 2 (n = 43, 2020-2021), were utilized to assess the signature's association with immunotherapy response and survival. Diagnostic efficiency was evaluated using the area under the receiver operating characteristic curve (AUC), and survival outcomes were analyzed via the Kaplan-Meier method. The TCIA cohort (n = 29) was included to evaluate the immune infiltration landscape of the radiomics signature subgroups using both CT images and mRNA sequencing data | ||
| 520 | |a RESULTS: Nine radiomics features were identified for signature development, exhibiting excellent discriminative performance in both the training (AUC: 0.851, 95%CI: 0.782, 0.919) and validation cohorts (AUC: 0.816, 95%CI: 0.706, 0.926). The radscore, calculated using the signature, demonstrated strong predictive abilities for objective response in immunotherapy cohorts (AUC: 0.734, 95%CI: 0.662, 0.806; AUC: 0.724, 95%CI: 0.572, 0.877). Additionally, the radscore showed a significant association with PFS and OS, with GC patients with a low radscore experiencing a significant survival benefit from immunotherapy. Immune infiltration analysis revealed significantly higher levels of CD8 + T cells, activated CD4 + B cells, and TNFRSF18 expression in the low radscore group, while the high radscore group exhibited higher levels of T cells regulatory and HHLA2 expression | ||
| 520 | |a CONCLUSION: This study developed a robust radiomics signature with the potential to serve as a non-invasive biomarker for GC's MSI status and immunotherapy response, demonstrating notable links to post-immunotherapy PFS and OS. Additionally, distinct immune profiles were observed between low and high radscore groups, highlighting their potential clinical implications | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a Gastric cancer | |
| 650 | 4 | |a Immunotherapy | |
| 650 | 4 | |a MSI | |
| 650 | 4 | |a Radiomics signature | |
| 650 | 4 | |a mRNA-seq | |
| 650 | 7 | |a HHLA2 protein, human |2 NLM | |
| 650 | 7 | |a Immunoglobulins |2 NLM | |
| 700 | 1 | |a Yang, Shuo |e verfasserin |4 aut | |
| 700 | 1 | |a Liu, Xing |e verfasserin |4 aut | |
| 700 | 1 | |a Zhang, Yu-Yuan |e verfasserin |4 aut | |
| 700 | 1 | |a Wang, Rui |e verfasserin |4 aut | |
| 700 | 1 | |a Wang, Jia-Xing |e verfasserin |4 aut | |
| 700 | 1 | |a Qiu, Qing-Ya |e verfasserin |4 aut | |
| 700 | 1 | |a Gao, Yu |e verfasserin |4 aut | |
| 700 | 1 | |a Lv, Dong-Bo |e verfasserin |4 aut | |
| 700 | 1 | |a Li, Li-Ming |e verfasserin |4 aut | |
| 700 | 1 | |a Luo, Cheng-Long |e verfasserin |4 aut | |
| 700 | 1 | |a Hu, Zhi-Wei |e verfasserin |4 aut | |
| 700 | 1 | |a Li, Zhen |e verfasserin |4 aut | |
| 700 | 1 | |a Lyu, Pei-Jie |e verfasserin |4 aut | |
| 700 | 1 | |a Liang, Pan |e verfasserin |4 aut | |
| 700 | 1 | |a Gao, Jian-Bo |e verfasserin |4 aut | |
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