MRI radiomics and nutritional-inflammatory biomarkers: a powerful combination for predicting progression-free survival in cervical cancer patients undergoing concurrent chemoradiotherapy
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View abstract on PubMed
Summary
This summary is machine-generated.A new model integrating clinical data, MRI radiomics, and nutritional markers accurately predicts progression-free survival in cervical cancer patients undergoing chemoradiotherapy. This tool helps identify high-risk individuals for personalized treatment strategies.
Area Of Science
- Oncology
- Radiology
- Biomarkers
Background
- Cervical cancer (CC) patients undergoing concurrent chemoradiotherapy (CCRT) have variable prognoses.
- Accurate prediction of progression-free survival (PFS) is crucial for personalized treatment strategies.
Purpose Of The Study
- To develop and validate a predictive model for PFS in CC patients treated with CCRT.
- The model integrates clinical features, MRI radiomics, and nutritional-inflammatory biomarkers.
- To identify high-risk patients for tailored therapeutic interventions.
Main Methods
- Retrospective analysis of 188 CC patients (132 training, 56 validation).
- Collection of clinical data, systemic inflammatory markers, and immune-nutritional indices.
- Extraction and selection of radiomic features from MRI; development and evaluation of five predictive models.
- Creation and validation of a nomogram using ROC curves, calibration plots, and decision curve analysis.
Main Results
- The integrated model (Model 5) combining clinical features, Systemic Immune-Inflammation Index (SII), Prognostic Nutritional Index (PNI), and MRI radiomics demonstrated superior performance.
- Achieved high C-index values: 0.833 (training) and 0.789 (validation).
- The derived nomogram accurately stratified patients by risk, showing high AUCs for 1-, 3-, and 5-year PFS in both sets.
Conclusions
- An integrated model incorporating clinical, radiomic, and nutritional-inflammatory data provides robust PFS prediction in CC patients undergoing CCRT.
- The developed nomogram offers precise prognostic insights, facilitating personalized patient management and treatment decisions.
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