ArticleBMC cancer2024
Pretreatment multiparametric MRI radiomics-integrated clinical hematological biomarkers can predict early rapid metastasis in patients with nasopharyngeal carcinoma.
Article in BMC cancer, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed, 6 citations in OpenAlex.
- Development and validation of a metabolic syndrome-based nomogram to predict 1-year metastasis after radiotherapy in locally advanced nasopharyngeal carcinoma.Annals of medicine · 2026Article
- Prognostic prediction in nasopharyngeal carcinoma using multi-region interaction features based on MRI habitat analysis.Quantitative imaging in medicine and surgery · 2026Article
- Prediction of local recurrence in cohorts of head and neck cancer patients after intensity-modulated radiation therapy based on CT radiomics: A double-center observation study.Translational oncology · 2026Article
- Noninvasive prediction of lymphovascular invasion in rectal cancer without lymph node metastasis using a SHAP-interpretable combined model integrating MRI radiomics features and clinical immune-inflammatory biomarkers: a bicenter study.Cancer imaging : the official publication of the International Cancer Imaging Society · 2025Article
- Article
- Dynamic contrast-enhanced magnetic resonance imaging-based radiomics for predicting programmed death ligand 1 expression in nasopharyngeal carcinoma.BMC medical imaging · 2025Article
- Enhanced MRI Radiomics Based Model for Predicting Recurrence or Metastasis of Nasopharyngeal Cancer (NC) Undergoing Concurrent Chemoradiotherapy: A Retrospective Study.Cancer control : journal of the Moffitt Cancer CenterArticle
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6 authors at 3 institutions in 1 country.
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Abstract
backgroundTo establish and validate a predictive model combining pretreatment multiparametric MRI-based radiomic signatures and clinical characteristics for the risk evaluation of early rapid metastasis in nasopharyngeal carcinoma (NPC) patients.
methodsThe cutoff time was used to randomly assign 219 consecutive patients who underwent chemoradiation treatment to the training group (n = 154) or the validation group (n = 65). Pretreatment multiparametric magnetic resonance (MR) images of individuals with NPC were employed to extract 428 radiomic features. LASSO regression analysis was used to select radiomic features related to early rapid metastasis and develop the Rad-score. Blood indicators were collected within 1 week of pretreatment. To identify independent risk variables for early rapid metastasis, univariate and multivariate logistic regression analyses were employed. Finally, multivariate logistic regression analysis was applied to construct a radiomics and clinical prediction nomogram that integrated radiomic features and clinical and blood inflammatory predictors.
resultsThe NLR, T classification and N classification were found to be independent risk indicators for early rapid metastasis by multivariate logistic regression analysis. Twelve features associated with early rapid metastasis were selected by LASSO regression analysis, and the Rad-score was calculated. The AUC of the Rad-score was 0.773. Finally, we constructed and validated a prediction model in combination with the NLR, T classification, N classification and Rad-score. The area under the curve (AUC) was 0.936 (95% confidence interval (95% CI): 0.901-0.971), and in the validation cohort, the AUC was 0.796 (95% CI: 0.686-0.905).
conclusionsA predictive model that integrates the NLR, T classification, N classification and MR-based radiomics for distinguishing early rapid metastasis may serve as a clinical risk stratification tool for effectively guiding individual management.
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