Dataset Information

Development and validation of a practical score to predict 3-year distant metastatic free survival in nasopharyngeal carcinoma incorporating the number of lymph node regions.

ABSTRACT:

Introduction

The improvement in diagnosis and treatment for nasopharyngeal carcinoma (NPC) has shifted the pattern of failure toward distant metastasis. This study aimed to develop a simplified prognostic scoring model to predict distant metastatic free survival (DMFS) for NPC patients.

Materials and methods

Patients with non-metastatic NPC were identified from a retrospective cohort diagnosed between 2010 and 2018. Flexible parametric survival analysis was used to identify potential predictors for DMFS and establish a scoring model. The prognostic accuracy between the 8th AJCC system and the scoring model was compared using Harrell's C-index.

Results

Of the total 393 patients, the median follow-up time was 85 months. The 3-year DMFS rate was 83.3%. Gender, T-stage, pre-EBV (cut-off 2300 copies/ml), and the number of metastatic lymph node regions were identified as independent risk factors for distant metastasis and were included in the final scoring model. Our established model achieved a high C-index in predicting DMFS (0.79) and was well-calibrated. The score divided patients into two categories: low-risk (score 0-4) and high-risk (score 5-7), corresponding with the predicted 3-year DMFS of 96% and 64.5%, respectively.

Conclusions

A feasible and applicative prognostic score was established and validated to discriminate NPC patients into low- and high-risk groups.

SUBMITTER: Jaruthien T

PROVIDER: S-EPMC11349101 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Development and validation of a practical score to predict 3-year distant metastatic free survival in nasopharyngeal carcinoma incorporating the number of lymph node regions.

Jaruthien Thitiporn T Lertbutsayanukul Chawalit C Jittapiromsak Nutchawan N Sriyook Aniwat A Kulalert Prapasri P Tantiyavarong Pichaya P Kitpanit Sarin S Kannarunimit Danita D Chakkabat Chakkapong C Prayongrat Anussara A

PloS one 20240827 8

<h4>Introduction</h4>The improvement in diagnosis and treatment for nasopharyngeal carcinoma (NPC) has shifted the pattern of failure toward distant metastasis. This study aimed to develop a simplified prognostic scoring model to predict distant metastatic free survival (DMFS) for NPC patients.<h4>Materials and methods</h4>Patients with non-metastatic NPC were identified from a retrospective cohort diagnosed between 2010 and 2018. Flexible parametric survival analysis was used to identify potent ...[more]

PMID: 39190649

Similar Datasets

Project description: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.

Project description:BackgroundThe Node Reporting and Data System (Node-RADS) is a recently proposed classification system for the categorization of lymph nodes in radiological images. This study was conducted to retrospectively evaluate the diagnostic accuracy of the Node-RADS score for metastatic cervical lymph nodes on magnetic resonance imaging (MRI) of patients with nasopharyngeal carcinoma (NPC).MethodsWe retrospectively analyzed cervical lymph nodes of NPC cases. Two radiologists independently evaluated each lymph node on the MRI scans using Node-RADS. Interobserver agreement between 2 radiologists for Node-RADS score assessment was evaluated by linear weighted kappa statistics. The correlation between metastasis and the Node-RADS score of each lymph node was analyzed using multivariate regression analysis. To investigate the diagnostic performance of the Node-RADS score, we further conducted receiver operating characteristic curve analysis. Correspondently, the sensitivity, specificity, positive predictive value, and negative predictive value of each different cutoff (>1, >2, >3, and >4) were computed.ResultsIn all, 119 patients with NPC were assessed, including 203 cervical lymph nodes consisting of 140 (69%) of 203 metastatic and 63 (31%) of 203 benign. The kappa agreement between the 2 readers for the Node-RADS score was 0.863 (95% CI = 0.830-0.897, P < .001). Node-RADS score on MRI scan was shown to be an independent predictive factor of lymph node metastasis after multivariate regression analysis (odds ratio [OR] = 6.745, 95% CI = 3.964-11.474, P < .001). Node-RADS achieved an area under the curve (AUC) of 0.950 (95% CI = 0.921-0.979) in diagnosing metastatic lymph nodes. When Node-RADS >2 was identified as the best cutoff based on balanced values, the sensitivity and positive predictive value were 0.92 and 0.94, respectively.ConclusionsOur study suggests that the Node-RADS score has high accuracy in predicting NPC cervical lymph node metastasis. Nevertheless, this conclusion requires confirmation in a larger cohort of patients with NPC.

Project description:ObjectiveTo establish and substantiate MRI-based radiomic models to predict the treatment response of metastatic cervical lymph node to radiochemotherapy in patients with nasopharyngeal carcinoma (NPC).MethodsA total of 145 consecutive patients with NPC were enrolled including 102 in primary cohort and 43 in validation cohort. Metastatic lymph nodes were diagnosed according to radiologic criteria and treatment response was evaluated according to the Response Evaluation Criteria in Solid Tumors. A total of 2704 radiomic features were extracted from contrast-enhanced T1 weighted imaging (CE- T1WI) and T2 weighted imaging (T2WI) for each patient, and were selected to construct radiomic signatures for CE-T1WI, T2WI, and combined CE-T1WI and T2WI, respectively. The area under curve (AUC) of receiver operating characteristic, sensitivity, specificity, and accuracy were used to estimate the performance of these radiomic models in predicting treatment response of metastatic lymph node.ResultsNo significant difference of AUC was found among radiomic signatures of CE-T1WI, T2WI, and combined CE-T1WI and T2WI in the primary and validation cohorts (all p > 0.05). For combined CE-T1WI and T2WI data set, 12 features were selected to develop the radiomic signature. The AUC, sensitivity, specificity, and accuracy were 0.927 (0.878-0.975), 0.911 (0.804-0.970), 0.826 (0.686-0.922), and 0.872 (0.792-0.930) in primary cohort, and were 0.772 (0.624-0.920), 0.792 (0.578-0.929), 0.790 (0.544-0.939), and 0.791 (0.640-0.900) in validation cohort.ConclusionMRI-based radiomic models were developed to predict the treatment response of metastatic cervical lymph nodes to radiochemotherapy in patients with NPC, which might facilitate individualized therapy for metastatic lymph nodes before treatment.Advances in knowledgePredicting the response in patients with NPC before treatment may allow more individualizing therapeutic strategy and avoid unnecessary side-effects and costs. Radiomic features extracted from metastatic cervical lymph nodes showed promising application for predicting the treatment response in NPC.

Project description:BackgroundImmune checkpoint blockade (ICB) elicits a strong and durable therapeutic response, but its application is limited by disparate responses and its associated immune-related adverse events (irAEs). Previously, in a murine model of lymph node (LN) metastasis, we showed that intranodal administration of chemotherapeutic agents using a lymphatic drug delivery system (LDDS) elicits stronger therapeutic responses in comparison to systemic drug delivery approaches, while minimizing systemic toxicity, due to its improved pharmacokinetic profile at the intended site. Importantly, the LN is a reservoir of immunotherapeutic targets. We therefore hypothesized that metastatic LN-targeted ICB can amplify anti-tumor response and uncouple it from ICB-induced irAEs.MethodsTo test our hypothesis, models of LN and distant metastases were established with luciferase expressing LM8 cells in MXH10/Mo-lpr/lpr mice, a recombinant inbred strain of mice capable of recapitulating ICB-induced interstitial pneumonia. This model was used to interrogate ICB-associated therapeutic response and immune related adverse events (irAEs) by in vivo imaging, high-frequency ultrasound imaging and histopathology. qPCR and flowcytometry were utilized to uncover the mediators of anti-tumor immunity.ResultsTumor-bearing LN (tbLN)-directed CTLA4 blockade generated robust anti-tumor response against local and systemic metastases, thereby improving survival. The anti-tumor effects were accompanied by an upregulation of effector CD8T cells in the tumor-microenvironment and periphery. In comparison, non-specific CTLA4 blockade was found to elicit weaker anti-tumor effect and exacerbated ICI-induced irAEs, especially interstitial pneumonia. Together these data highlight the importance of tbLN-targeted checkpoint blockade for efficacious response.ConclusionsIntranodal delivery of immune checkpoint inhibitors to metastatic LN can potentiate therapeutic response while minimizing irAEs stemming from systemic lowering of immune activation threshold.

Dataset Information

Development and validation of a practical score to predict 3-year distant metastatic free survival in nasopharyngeal carcinoma incorporating the number of lymph node regions.

Introduction

Materials and methods

Results

Conclusions

Publications

Development and validation of a practical score to predict 3-year distant metastatic free survival in nasopharyngeal carcinoma incorporating the number of lymph node regions.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets