Radiomic signatures reveal multiscale intratumor heterogeneity associated with tissue tolerance and survival in re-irradiated nasopharyngeal carcinoma: a multicenter study
Background Post-radiation nasopharyngeal necrosis (PRNN) is a severe adverse event following re-radiotherapy for patients with locally recurrent nasopharyngeal carcinoma (LRNPC) and associated with decreased survival. Biological heterogeneity in recurrent tumors contributes to the different risks of PRNN. Radiomics can be used to mine high-throughput non-invasive image features to predict clinical outcomes and capture underlying biological functions. We aimed to develop a radiogenomic signature for the pre-treatment prediction of PRNN to guide re-radiotherapy in patients with LRNPC. Methods This multicenter study included 761 re-irradiated patients with LRNPC at four centers in NPC endemic area and divided them into training, internal validation, and external validation cohorts. We built a machine learning (random forest) radiomic signature based on the pre-treatment multiparametric magnetic resonance images for predicting PRNN following re-radiotherapy. We comprehensively assessed the performance of the radiomic signature. Transcriptomic sequencing and gene set enrichment analyses were conducted to identify the associated biological processes. Results The radiomic signature showed discrimination of 1-year PRNN in the training, internal validation, and external validation cohorts (area under the curve (AUC) 0.713–0.756). Stratified by a cutoff score of 0.735, patients with high-risk signature had higher incidences of PRNN than patients with low-risk signature (1-year PRNN rates 42.2–62.5% vs. 16.3–18.8%, P < 0.001). The signature significantly outperformed the clinical model (P < 0.05) and was generalizable across different centers, imaging parameters, and patient subgroups. The radiomic signature had prognostic value concerning its correlation with PRNN-related deaths (hazard ratio (HR) 3.07–6.75, P < 0.001) and all causes of deaths (HR 1.53–2.30, P < 0.01). Radiogenomics analyses revealed associations between the radiomic signature and signaling pathways involved in tissue fibrosis and vascularity. Conclusions We present a radiomic signature for the individualized risk assessment of PRNN following re-radiotherapy, which may serve as a noninvasive radio-biomarker of radiation injury-associated processes and a useful clinical tool to personalize treatment recommendations for patients with LANPC..
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2023 |
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| Erschienen: |
2023 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:21 |
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| Enthalten in: |
BMC medicine - 21(2023), 1 vom: 27. Nov. |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Liu, Ting [VerfasserIn] |
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| Links: |
Volltext [kostenfrei] |
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| Themen: |
Nasopharyngeal necrosis |
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| Anmerkungen: |
© The Author(s) 2023 |
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| doi: |
10.1186/s12916-023-03164-3 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| PPN (Katalog-ID): |
SPR053880463 |
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| 100 | 1 | |a Liu, Ting |e verfasserin |4 aut | |
| 245 | 1 | 0 | |a Radiomic signatures reveal multiscale intratumor heterogeneity associated with tissue tolerance and survival in re-irradiated nasopharyngeal carcinoma: a multicenter study |
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| 520 | |a Background Post-radiation nasopharyngeal necrosis (PRNN) is a severe adverse event following re-radiotherapy for patients with locally recurrent nasopharyngeal carcinoma (LRNPC) and associated with decreased survival. Biological heterogeneity in recurrent tumors contributes to the different risks of PRNN. Radiomics can be used to mine high-throughput non-invasive image features to predict clinical outcomes and capture underlying biological functions. We aimed to develop a radiogenomic signature for the pre-treatment prediction of PRNN to guide re-radiotherapy in patients with LRNPC. Methods This multicenter study included 761 re-irradiated patients with LRNPC at four centers in NPC endemic area and divided them into training, internal validation, and external validation cohorts. We built a machine learning (random forest) radiomic signature based on the pre-treatment multiparametric magnetic resonance images for predicting PRNN following re-radiotherapy. We comprehensively assessed the performance of the radiomic signature. Transcriptomic sequencing and gene set enrichment analyses were conducted to identify the associated biological processes. Results The radiomic signature showed discrimination of 1-year PRNN in the training, internal validation, and external validation cohorts (area under the curve (AUC) 0.713–0.756). Stratified by a cutoff score of 0.735, patients with high-risk signature had higher incidences of PRNN than patients with low-risk signature (1-year PRNN rates 42.2–62.5% vs. 16.3–18.8%, P < 0.001). The signature significantly outperformed the clinical model (P < 0.05) and was generalizable across different centers, imaging parameters, and patient subgroups. The radiomic signature had prognostic value concerning its correlation with PRNN-related deaths (hazard ratio (HR) 3.07–6.75, P < 0.001) and all causes of deaths (HR 1.53–2.30, P < 0.01). Radiogenomics analyses revealed associations between the radiomic signature and signaling pathways involved in tissue fibrosis and vascularity. Conclusions We present a radiomic signature for the individualized risk assessment of PRNN following re-radiotherapy, which may serve as a noninvasive radio-biomarker of radiation injury-associated processes and a useful clinical tool to personalize treatment recommendations for patients with LANPC. | ||
| 650 | 4 | |a Recurrent nasopharyngeal carcinoma |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Re-radiotherapy |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Nasopharyngeal necrosis |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Radiomics |7 (dpeaa)DE-He213 | |
| 700 | 1 | |a Dong, Di |4 aut | |
| 700 | 1 | |a Zhao, Xun |4 aut | |
| 700 | 1 | |a Ou, Xiao-Min |4 aut | |
| 700 | 1 | |a Yi, Jun-Lin |4 aut | |
| 700 | 1 | |a Guan, Jian |4 aut | |
| 700 | 1 | |a Zhang, Ye |4 aut | |
| 700 | 1 | |a Xiao-Fei, Lv |4 aut | |
| 700 | 1 | |a Xie, Chuan-Miao |4 aut | |
| 700 | 1 | |a Luo, Dong-Hua |4 aut | |
| 700 | 1 | |a Sun, Rui |4 aut | |
| 700 | 1 | |a Chen, Qiu-Yan |4 aut | |
| 700 | 1 | |a Xing, Lv |4 aut | |
| 700 | 1 | |a Guo, Shan-Shan |4 aut | |
| 700 | 1 | |a Liu, Li-Ting |4 aut | |
| 700 | 1 | |a Lin, Da-Feng |4 aut | |
| 700 | 1 | |a Chen, Yan-Zhou |4 aut | |
| 700 | 1 | |a Lin, Jie-Yi |4 aut | |
| 700 | 1 | |a Luo, Mei-Juan |4 aut | |
| 700 | 1 | |a Yan, Wen-Bin |4 aut | |
| 700 | 1 | |a He, Mei-Lin |4 aut | |
| 700 | 1 | |a Mao, Meng-Yuan |4 aut | |
| 700 | 1 | |a Zhu, Man-Yi |4 aut | |
| 700 | 1 | |a Chen, Wen-Hui |4 aut | |
| 700 | 1 | |a Shen, Bo-Wen |4 aut | |
| 700 | 1 | |a Wang, Shi-Qian |4 aut | |
| 700 | 1 | |a Li, Hai-Lin |4 aut | |
| 700 | 1 | |a Zhong, Lian-Zhen |4 aut | |
| 700 | 1 | |a Hu, Chao-Su |4 aut | |
| 700 | 1 | |a Wu, De-Hua |4 aut | |
| 700 | 1 | |a Mai, Hai-Qiang |4 aut | |
| 700 | 1 | |a Tian, Jie |4 aut | |
| 700 | 1 | |a Tang, Lin-Quan |0 (orcid)0000-0002-9613-7305 |4 aut | |
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