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Radiomics on multi-modalities MR sequences can subtype patients with non-metastatic nasopharyngeal carcinoma (NPC) into distinct survival subgroups

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A Correction to this article was published on 26 April 2019

This article has been updated

Abstract

Objectives

To explore and evaluate the feasibility of radiomics in stratifying nasopharyngeal carcinoma (NPC) into distinct survival subgroups through multi-modalities MRI.

Methods

A total of 658 patients (training cohort: 424; validation cohort: 234) with non-metastatic NPC were enrolled in the retrospective analysis. Each slice was considered as a sample and 4863 radiomics features on the tumor region were extracted from T1-weighted, T2-weighted, and contrast-enhanced T1-weighted MRI. Consensus clustering and manual aggregation were performed on the training cohort to generate a baseline model and classification reference used to train a support vector machine classifier. The risk of each patient was defined as the maximum risk among the slices. Each patient in the validation cohort was assigned to the risk model using the trained classifier. Harrell’s concordance index (C-index) was used to measure the prognosis performance, and differences between subgroups were compared using the log-rank test.

Results

The training cohort was clustered into four groups with distinct survival patterns. Each patient was assigned to one of the four groups according to the estimated risk. Our method gave a performance (C-index = 0.827, p < .004 and C-index = 0.814, p < .002) better than the T-stage (C-index = 0.815, p = .002 and C-index = 0.803, p = .024), competitive to and more stable than the TNM staging system (C-index = 0.842, p = .003 and C-index = 0.765, p = .050) in the training cohort and the validation cohort.

Conclusions

Through investigating a large one-institutional cohort, the quantitative multi-modalities MRI image phenotypes reveal distinct survival subtypes.

Key Points

• Radiomics phenotype of MRI revealed the subtype of nasopharyngeal carcinoma (NPC) patients with distinct survival patterns.

• The slice-wise analysis method on MRI helps to stratify patients and provides superior prognostic performance over the TNM staging method.

• Risk estimation using the highest risk among slices performed better than using the majority risk in prognosis.

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Change history

  • 26 April 2019

    The original version of this article, published on 14 March 2019, unfortunately contained a mistake.

Abbreviations

AJCC:

American Joint Committee on Cancer

CI:

Confidence interval

EBV:

Epstein-Barr virus

GC:

Gastric cancer

IMRT:

Intensity-modulated radiation therapy

LRFS:

Locoregional recurrence-free survival

NPC:

Nasopharyngeal carcinoma

PGL:

Primary gastric lymphoma

ROI:

Region of interest

TNM:

Tumor, node and metastasis

UICC:

Union for International Cancer Control

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Funding

This work was supported by grants from the National Natural Science Foundation of China (no.61771007, no.81572652), Health & Medical Collaborative Innovation Project of Guangzhou City, China (grants 201604020003, 201803010021), Science and Technology Planning Projects of Guangdong Province (2016A010101013, 2017B020226004), and the Fundamental Research Fund for the Central Universities (2017ZD051).

Author information

Author notes

  1. En-Hong Zhuo and Wei-Jing Zhang contributed equally to this work.

Authors and Affiliations

  1. School of Computer Science and Engineering, South China University of Technology, Guangzhou, 510006, Guangdong Province, People’s Republic of China

    En-Hong Zhuo & Hong-Min Cai

  2. State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Sun Yat-sen University Cancer Center, No.651 Dongfeng east road, Yuexiu District, Guangzhou, Guangdong province, People’s Republic of China

    Wei-Jing Zhang, Hao-Jiang Li, Bing-Zhong Jing, Jian Zhou, Chun-Yan Cui, Ming-Yuan Chen, Ying Sun & Li-Zhi Liu

  3. Department of Radiation Oncology, Cancer Center, First People’s Hospital of Foshan, Foshan, 528000, Guangdong Province, People’s Republic of China

    Guo-Yi Zhang

  4. Guangdong Provincial Key Lab of Computational Intelligence and Cyberspace Information, South China University of Technology, Guangzhou, People’s Republic of China

    Hong-Min Cai

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  1. En-Hong Zhuo
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Corresponding authors

Correspondence to Li-Zhi Liu or Hong-Min Cai.

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The scientific guarantor of this publication is Hong-min Cai.

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The authors of this manuscript declare no relationships with any companies whose products or services may be related to the subject matter of the article.

Statistics and biometry

No complex statistical methods were necessary for this paper.

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Written informed consent was waived by the Institutional Review Board.

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• performed at one institution

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Zhuo, EH., Zhang, WJ., Li, HJ. et al. Radiomics on multi-modalities MR sequences can subtype patients with non-metastatic nasopharyngeal carcinoma (NPC) into distinct survival subgroups. Eur Radiol 29, 5590–5599 (2019). https://doi.org/10.1007/s00330-019-06075-1

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