ArticleCancer imaging : the official publication of the International Cancer Imaging Society2023
Regional lymph node density-based nomogram predicts prognosis in nasopharyngeal carcinoma patients without distant metastases.
Article in Cancer imaging : the official publication of the International Cancer Imaging Society, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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6 citing papers in PubMed.
- Validation and refinement of the N classification for nasopharyngeal carcinoma based on the 9th version AJCC/UICC TNM staging system in non-endemic regions of China: a multicenter cohort study.Clinical and translational radiation oncology · 2027Article
- Prognostic Value and Therapeutic Implications of MRI-Based Negative Lymph Nodes in Nasopharyngeal Carcinoma.Cancer medicine · 2026Article
- The application of grayscale quantitative analysis in the differential diagnosis of necrosis between cervical lymph node metastases in nasopharyngeal carcinoma and tuberculous lymphadenitis.Frontiers in oncology · 2026Article
- Surgical Process Modeling of Workflow and Performance in Image-Guided Bronchoscopy.Annals of biomedical engineering · 2025Article
- Survival benefits in non-small cell lung cancer during the immune checkpoint inhibitor era: integrating lymph node burden for prognostic precision.Translational lung cancer research · 2025Article
- Computed tomography-based radiomics modeling to predict patient overall survival in cervical cancer with intensity-modulated radiotherapy combined with concurrent chemotherapy.The Journal of international medical research · 2025Observational
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Abstract
backgroundNasopharyngeal carcinoma (NPC) is a relatively common type of cancer in Southern China, with local recurrence or distant metastases even after radical treatment; consequently, it is critical to identify the patients at higher risk for these events beforehand. This study aimed to assess the prognostic value of regional lymph node density (RLND) associated nomograms in NPC and to evaluate the utility of nomograms in risk stratification.
methodsA total of 610 NPC patients without distant metastases (425 in the training and 185 in the validation cohort) were enrolled. The MRI-identified nodal features and clinical characteristics were documented, and the RLND was calculated. Cox analyses were conducted to identify prognostic-associated factors. Nomograms were generated based on the multivariate analysis results. The predictive accuracy and discriminative ability of the nomogram models were determined using the concordance index (C-index), receiver operating characteristic (ROC) curve, and calibration curve; the results were compared with those of the tumor-node-metastasis (TNM) classification. Decision curve analysis (DCA) and C-index were used to assess the prognostic effect and added discriminative ability of RLND. We also estimated the optimal RLND-based nomogram score cut-off values for survival prediction.
resultsRLND was an independent predictor of overall survival (OS) and disease-free survival (DFS), with hazard ratios of 1.36 and 1.30, respectively. RLND was utilized in the construction of nomograms, alongside other independent prognostic factors. The RLND-based nomogram models presented a more effective discriminative ability than the TNM classification for predicting OS (C-index, 0.711 vs. 0.680) and DFS (C-index, 0.681 vs. 0.669), with favorable calibration and consistency. The comparison of C-index values between the nomogram models with and without RLND provided substantiation of the crucial role RLND plays in these models. DCA confirmed the satisfactory clinical practicability of RLND. Moreover, the nomograms were used to categorize the patients into three groups (high-, middle-, and low-risk), and the Kaplan-Meier curves showed significant differences in prognosis between them (p < 0.05). These results were verified in the validation cohort.
conclusionRLND stands as a robust prognostic factor in NPC. The RLND-based nomograms excel in predicting survival, surpassing the TNM classification.
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