Dataset Information

Development of a Nomogram Model for Treatment of Nonmetastatic Nasopharyngeal Carcinoma.

ABSTRACT:

Importance

Because of tumor heterogeneity, overall survival (OS) differs significantly among individuals with nasopharyngeal carcinoma (NPC), even among those with the same clinical stage. Relying solely on TNM staging to guide treatment remains imperfect.

Objectives

To establish a comprehensive nomogram to estimate individualized OS and to explore stratified treatment regimens for risk subgroups in nonmetastatic NPC.

Design, setting, and participants

This cohort study included 8093 patients diagnosed with NPC at a single center in China from April 2009 to December 2015. The sample was split into a training cohort (5398 participants [66.7%]) and validation cohort (2695 [33.3%]). Data were analyzed in May 2020.

Exposures

Age, T stage, N stage, Epstein-Barr virus (EBV) DNA level, serum lactate dehydrogenase (LDH) levels, and albumin (ALB) levels.

Main outcomes and measures

The primary end point was OS. The nomogram for estimating OS was generated based on multivariate Cox proportional hazards regression. The performance of the nomogram was quantified using Harrell concordance index (C index), the area under the curve (AUC) of the receiver operating characteristic curve, and a calibration curve. OS rates were established using the Kaplan-Meier method, and intersubgroup differences were examined by the log-rank test.

Results

Among the 8093 participants, 5688 (70.3%) were men, and the median age at diagnosis was 45 years (range, 7-85 years). Six variables (age, T stage, N stage, EBV DNA levels, LDH levels, and ALB levels) were identified through multivariate Cox regression and incorporated into a nomogram to estimate OS. The resulting nomogram showed excellent discriminative ability and significantly outperformed the eighth edition of the American Joint Committee on Cancer/Union for International Cancer Control TNM staging system for estimating OS (C index, 0.716 [95% CI, 0.698-0.734] vs 0.643 [95% CI, 0.624-0.661]; P?Conclusions and relevanceIn this study, the proposed nomogram model enabled individualized prognostication of OS and could help to guide risk-adapted treatment for patients with nonmetastatic NPC.

SUBMITTER: Zhang LL

PROVIDER: S-EPMC7733160 | biostudies-literature | 2020 Dec

REPOSITORIES: biostudies-literature

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Publications

Development of a Nomogram Model for Treatment of Nonmetastatic Nasopharyngeal Carcinoma.

Zhang Lu-Lu LL Xu Fei F Song Di D Huang Meng-Yao MY Huang Yong-Shi YS Deng Qi-Ling QL Li Yi-Yang YY Shao Jian-Yong JY

JAMA network open 20201201 12

<h4>Importance</h4>Because of tumor heterogeneity, overall survival (OS) differs significantly among individuals with nasopharyngeal carcinoma (NPC), even among those with the same clinical stage. Relying solely on TNM staging to guide treatment remains imperfect.<h4>Objectives</h4>To establish a comprehensive nomogram to estimate individualized OS and to explore stratified treatment regimens for risk subgroups in nonmetastatic NPC.<h4>Design, setting, and participants</h4>This cohort study incl ...[more]

PMID: 33306119

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Project description:BackgroundTo establish a novel model using radiomics analysis of pre-treatment and post-treatment magnetic resonance (MR) images for prediction of progression-free survival in the patients with stage II-IVA nasopharyngeal carcinoma (NPC) in South China.MethodsOne hundred and twenty NPC patients who underwent chemoradiotherapy were enrolled (80 in the training cohort and 40 in the validation cohort). Acquiring data and screening features were performed successively. Totally 1133 radiomics features were extracted from the T2-weight images before and after treatment. Least absolute shrinkage and selection operator regression, recursive feature elimination algorithm, random forest, and minimum-redundancy maximum-relevancy (mRMR) method were used for feature selection. Nomogram discrimination and calibration were evaluated. Harrell's concordance index (C-index) and receiver operating characteristic (ROC) analyses were applied to appraise the prognostic performance of nomograms. Survival curves were plotted using Kaplan-Meier method.ResultsIntegrating independent clinical predictors with pre-treatment and post-treatment radiomics signatures which were calculated in conformity with radiomics features, we established a clinical-and-radiomics nomogram by multivariable Cox regression. Nomogram consisting of 14 pre-treatment and 7 post-treatment selected features has been proved to yield a reliable predictive performance in both training and validation groups. The C-index of clinical-and-radiomics nomogram was 0.953 (all P < 0.05), which was higher than that of clinical (0.861) or radiomics nomograms alone (based on pre-treatment statistics: 0.942; based on post-treatment statistics: 0.944). Moreover, we received Rad-score of pre-treatment named RS1 and post-treatment named RS2 and all were used as independent predictors to divide patients into high-risk and low-risk groups. Kaplan-Meier analysis showed that lower RS1 (less than cutoff value, - 1.488) and RS2 (less than cutoff value, - 0.180) were easier to avoid disease progression (all P < 0.01). It showed clinical benefit with decision curve analysis.ConclusionsMR-based radiomics measured the burden on primary tumor before treatment and the tumor regression after chemoradiotherapy, and was used to build a model to predict progression-free survival (PFS) in the stage II-IVA NPC patients. It can also help to distinguish high-risk patients from low-risk patients, thus guiding personalized treatment decisions effectively.

Project description:BackgroundInduction chemotherapy (ICT) plus concurrent chemoradiotherapy (CCRT) and CCRT alone were the optional treatment regimens in locoregionally advanced nasopharyngeal carcinoma (NPC) patients. Currently, the choice of them remains equivocal in clinical practice. We aimed to develop a deep learning-based model for treatment decision in NPC.MethodsA total of 1872 patients with stage T3N1M0 NPC were enrolled from four Chinese centres and received either ICT+CCRT or CCRT. A nomogram was constructed for predicting the prognosis of patients with different treatment regimens using multi-task deep learning radiomics and pre-treatment MR images, based on which an optimal treatment regimen was recommended. Model performance was assessed by the concordance index (C-index) and the Kaplan-Meier estimator.FindingsThe nomogram showed excellent prognostic ability for disease-free survival in both the CCRT (C-index range: 0.888-0.921) and ICT+CCRT (C-index range: 0.784-0.830) groups. According to the prognostic difference between treatments using the nomogram, patients were divided into the ICT-preferred and CCRT-preferred groups. In the ICT-preferred group, patients receiving ICT+CCRT exhibited prolonged survival over those receiving CCRT in the internal and external test cohorts (hazard ratio [HR]: 0.17, p<0.001 and 0.24, p=0.02); while the trend was opposite in the CCRT-preferred group (HR: 6.24, p<0.001 and 12.08, p<0.001). Similar results for treatment decision using the nomogram were obtained in different subgroups stratified by clinical factors and MR acquisition parameters.InterpretationOur nomogram could predict the prognosis of T3N1M0 NPC patients with different treatment regimens and accordingly recommend an optimal treatment regimen, which may serve as a potential tool for promoting personalized treatment of NPC.FundingNational Key R&D Program of China, National Natural Science Foundation of China, Beijing Natural Science Foundation, Strategic Priority Research Program of CAS, Project of High-Level Talents Team Introduction in Zhuhai City, Beijing Natural Science Foundation, Beijing Nova Program, Youth Innovation Promotion Association CAS.

Dataset Information

Development of a Nomogram Model for Treatment of Nonmetastatic Nasopharyngeal Carcinoma.

Importance

Objectives

Design, setting, and participants

Exposures

Main outcomes and measures

Results

Publications

Development of a Nomogram Model for Treatment of Nonmetastatic Nasopharyngeal Carcinoma.

Similar Datasets

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