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A dynamic nomogram combining tumor stage and magnetic resonance imaging features to predict the response to induction chemotherapy in locally advanced nasopharyngeal carcinoma

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Abstract

Objectives

To establish an effective dynamic nomogram combining magnetic resonance imaging (MRI) findings of primary tumor and regional lymph nodes with tumor stage for the pretreatment prediction of induction chemotherapy (IC) response in locoregionally advanced nasopharyngeal carcinoma (LANPC).

Methods

A total of 498 LANPC patients (372 in the training and 126 in the validation cohort) with MRI information were enrolled. All patients were classified as “favorable responders” and “unfavorable responders” according to tumor response to IC. A nomogram for IC response was built based on the results of the logistic regression model. Also, the Cox regression analysis was used to identify the independent prognostic factors of disease-free survival (DFS).

Results

After two cycles of IC, 340 patients were classified as “favorable responders” and 158 patients as “unfavorable responders.” Calibration curves revealed satisfactory agreement between the predicted and the observed probabilities. The nomogram achieved an AUC of 0.855 (95% CI, 0.781–0.930) for predicting IC response, which outperformed TNM staging (AUC, 0.661; 95% CI 0.565–0.758) and the MRI feature-based model alone (AUC, 0.744; 95% CI 0.650–0.839) in the validation cohort. The nomogram was used to categorize patients into high- and low-response groups. An online dynamic model was built (https://nomogram-for-icresponse-prediction.shinyapps.io/DynNomapp/) to facilitate the application of the nomogram. In the Cox multivariate analysis, clinical stage, tumor necrosis, EBV DNA levels, and cervical lymph node numbers were independently associated with DFS.

Conclusions

The comprehensive nomogram incorporating MRI features and tumor stage could assist physicians in predicting IC response and formulating personalized treatment strategies for LANPC patients.

Key Points

• The nomogram can predict IC response in endemic LANPC.

• The nomogram combining tumor stage with MRI-based tumor features showed very good predictive performance.

• The nomogram was transformed into a web-based dynamic model to optimize clinical application.

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Abbreviations

AJCC:

American Joint Committee on Cancer

AUC:

An area under the receiver operating characteristic curve

CCRT:

Concurrent chemoradiation

C-index:

Harrell’s concordance index

CLN:

Cervical lymph node

CR:

Complete response

DCA:

Decision curve analysis

DFS:

Disease-free survival

DMFS:

Distant metastasis-free survival

ENE:

Extranodal extension

IC:

Induction chemotherapy

IMRT:

Intensity-modulated radiation therapy

LANPC:

Locoregionally advanced nasopharyngeal carcinoma

LN:

Lymph node

LRRFS:

Locoregional recurrence-free survival

MRI:

Magnetic resonance imaging

OS:

Overall survival

PD:

Progressive disease

PR:

Partial response

RLN:

Retropharyngeal lymph node

SD:

Stable disease

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Funding

This study has received funding by the Key Research and Development Program Project of Guangxi Zhuang Autonomous Region (Grant No. GuikeAB18221007), Youth Science Foundation Project of Guangxi Medical University (Grant No. GXMUYSF202122), the Independent Project of Key Laboratory of Early Prevention & Treatment for Regional High-Incidence-Tumor (Grant No. GKE-ZZ202014), the Self funded research project of Health Commission of Guangxi Zhuang Autonomous Region (Z20210913), and the Guangxi University Young and middle-aged Teachers (scientific research) basic ability Improvement project (Grant No. 2020KY03038).

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Author notes

  1. Yuting Jiang and Zhongguo Liang contributed equally to this work.

Authors and Affiliations

  1. Department of Radiation Oncology, Guangxi Medical University Cancer Hospital, Nanning, Guangxi, People’s Republic of China

    Yuting Jiang, Zhongguo Liang, Kaihua Chen, Ye Li, Jie Yang, Song Qu, Ling Li & Xiaodong Zhu

  2. Key Laboratory of Early Prevention and Treatment for Regional High-Incidence-Tumor, Guangxi Medical University, Ministry of Education, Nanning, Guangxi, People’s Republic of China

    Song Qu, Ling Li & Xiaodong Zhu

  3. Department of Oncology, Affiliated Wuming Hospital of Guangxi Medical University, Nanning, Guangxi, People’s Republic of China

    Xiaodong Zhu

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  1. Yuting Jiang
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Correspondence to Xiaodong Zhu.

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Jiang, Y., Liang, Z., Chen, K. et al. A dynamic nomogram combining tumor stage and magnetic resonance imaging features to predict the response to induction chemotherapy in locally advanced nasopharyngeal carcinoma. Eur Radiol 33, 2171–2184 (2023). https://doi.org/10.1007/s00330-022-09201-8

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