Predicting progression-free survival using dynamic contrast-enhanced imaging-based radiomics in advanced nasopharyngeal carcinoma patients treated with nimotuzumab
- Xin Jin 1, Wen-Zhu Li 1, Yi-Hao Guo 1, Gang Wu 2, Wei-Yuan Huang 3, Feng Chen 4
- Xin Jin 1, Wen-Zhu Li 1, Yi-Hao Guo 1
- 1Department of Radiology, Hainan Affiliated Hospital of Hainan Medical University (Hainan General Hospital), Haikou, China.
- 2Department of Radiotherapy Hainan Affiliated Hospital of Hainan Medical University (Hainan General Hospital), Haikou, China.
- 3Department of Radiology, Hainan Affiliated Hospital of Hainan Medical University (Hainan General Hospital), Haikou, China. weiyuanhuang@163.com.
- 4Department of Radiology, Hainan Affiliated Hospital of Hainan Medical University (Hainan General Hospital), Haikou, China. fenger0802@163.com.
- 0Department of Radiology, Hainan Affiliated Hospital of Hainan Medical University (Hainan General Hospital), Haikou, China.
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View abstract on PubMed
Summary
This summary is machine-generated.A new clinical-radiomics model combining imaging and EGFR levels accurately predicts nimotuzumab treatment response in locally advanced nasopharyngeal carcinoma (LA-NPC) patients, aiding personalized therapy.
Area Of Science
- Oncology
- Radiology
- Medical Imaging
Background
- Locally advanced nasopharyngeal carcinoma (LA-NPC) treatment response can be challenging to predict.
- Epidermal growth factor receptor (EGFR) expression is a known factor, but non-invasive predictive models are needed.
Purpose Of The Study
- To evaluate a radiomics model from dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) for predicting progression-free survival (PFS) in LA-NPC patients treated with nimotuzumab (NTZ).
- To compare the radiomics model's predictive value against a clinical model based on EGFR expression.
Main Methods
- 136 LA-NPC patients treated with NTZ were analyzed.
- Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) radiomics, clinical, and combined clinical-radiomics models were developed to predict PFS.
- Model performance was assessed using area under the curve, calibration, and DeLong tests.
Main Results
- EGFR expression level was the sole independent predictor of PFS (p < 0.05).
- Patients with higher EGFR expression (3+) had significantly longer PFS (HR = 3.025, p < 0.05).
- The combined clinical-radiomics model demonstrated superior predictive efficacy over individual models in both training and validation sets (AUCs > 0.83, p < 0.001).
Conclusions
- Clinical-radiomics models integrating DCE-MRI and EGFR levels effectively predict nimotuzumab efficacy in LA-NPC.
- This approach offers objective evidence for personalized treatment adjustments and improved patient outcomes.
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