Skip to main content

Advertisement

SpringerLink
Log in

Deep learning signatures reveal multiscale intratumor heterogeneity associated with biological functions and survival in recurrent nasopharyngeal carcinoma

  • Original Article
  • Published:
European Journal of Nuclear Medicine and Molecular Imaging Aims and scope Submit manuscript

Abstract

Purpose

How to discriminate different risks of recurrent nasopharyngeal carcinoma (rNPC) patients and guide individual treatment has become of great importance. This study aimed to explore the associations between deep learning signatures and biological functions as well as survival in (rNPC) patients.

Methods

A total of 420 rNPC patients with PET/CT imaging and follow-up of overall survival (OS) were retrospectively enrolled. All patients were randomly divided into a training set (n = 269) and test set (n = 151) with a 6:4 ratio. We constructed multi-modality deep learning signatures from PET and CT images with a light-weighted deep convolutional neural network EfficienetNet-lite0 and survival loss DeepSurvLoss. An integrated nomogram was constructed incorporating clinical factors and deep learning signatures from PET/CT. Clinical nomogram and single-modality deep learning nomograms were also built for comparison. Furthermore, the association between biological functions and survival risks generated from an integrated nomogram was analyzed by RNA sequencing (RNA-seq).

Results

The C-index of the integrated nomogram incorporating age, rT-stage, and deep learning PET/CT signature was 0.741 (95% CI: 0.688–0.794) in the training set and 0.732 (95% CI: 0.679–0.785) in the test set. The nomogram stratified patients into two groups with high risk and low risk in both the training set and test set with hazard ratios (HR) of 4.56 (95% CI: 2.80–7.42, p < 0.001) and 4.05 (95% CI: 2.21–7.43, p < 0.001), respectively. The C-index of the integrated nomogram was significantly higher than the clinical nomogram and single-modality nomograms. When stratified by sex, N-stage, or EBV DNA, risk prediction of our integrated nomogram was valid in all patient subgroups. Further subgroup analysis showed that patients with a low-risk could benefit from surgery and re-irradiation, while there was no difference in survival rates between patients treated by chemotherapy in the high-risk and low-risk groups. RNA sequencing (RNA-seq) of data further explored the mechanism of high- and low-risk patients from the genetic and molecular level.

Conclusion

Our study demonstrated that PET/CT-based deep learning signatures showed satisfactory prognostic predictive performance in rNPC patients. The nomogram incorporating deep learning signatures successfully divided patients into different risks and had great potential to guide individual treatment: patients with a low-risk were supposed to be treated with surgery and re-irradiation, while for high-risk patients, the application of palliative chemotherapy may be sufficient.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Similar content being viewed by others

References

  1. Bray F, Ferlay J, Soerjomataram I, Siegel RL, Torre LA, Jemal A. Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin. 2018;68:394–424.

    Article  Google Scholar 

  2. Lee AWM, Ma BBY, Ng WT, Chan ATC. Management of nasopharyngeal carcinoma: current practice and future perspective. J Clin Oncol. 2015;33:3356–64.

    Article  Google Scholar 

  3. Ng WT, Soong YL, Ahn YC, AlHussain H, Choi HCW, Corry J, et al. International recommendations on reirradiation by intensity modulated radiation therapy for locally recurrent nasopharyngeal carcinoma. Int J Radiat Oncol Biol Phys. 2021;110:682–95.

    Article  Google Scholar 

  4. Liu L-T, Chen Q-Y, Tang L-Q, Zhang L, Guo S-S, Guo L, et al. With or without reirradiation in advanced local recurrent nasopharyngeal carcinoma: a case-control study. BMC Cancer. 2016;16:774.

    Article  Google Scholar 

  5. Sun X-S, Liang Y-J, Jia G-D, Liu S-L, Liu L-T, Guo S-S, et al. Establishment of a prognostic nomogram to identify optimal candidates for local treatment among patients with local recurrent nasopharyngeal carcinoma. Oral Oncol. 2020;106:104711.

    Article  CAS  Google Scholar 

  6. Han F, Zhao C, Huang S-M, Lu L-X, Huang Y, Deng X-W, et al. Long-term outcomes and prognostic factors of re-irradiation for locally recurrent nasopharyngeal carcinoma using intensity-modulated radiotherapy. Clin Oncol. 2012;24:569–76.

    Article  Google Scholar 

  7. Bruixola G, Remacha E, Jiménez-Pastor A, Dualde D, Viala A, Montón JV, et al. Radiomics and radiogenomics in head and neck squamous cell carcinoma: potential contribution to patient management and challenges. Cancer Treat Rev. 2021;99:102263.

    Article  Google Scholar 

  8. Beig N, Singh S, Bera K, Prasanna P, Singh G, Chen J, et al. Sexually dimorphic radiogenomic models identify distinct imaging and biological pathways that are prognostic of overall survival in glioblastoma. Neuro-Oncol. 2021;23:251–63.

    Article  Google Scholar 

  9. Dong D, Zhang F, Zhong L-Z, Fang M-J, Huang C-L, Yao J-J, et al. Development and validation of a novel MR imaging predictor of response to induction chemotherapy in locoregionally advanced nasopharyngeal cancer: a randomized controlled trial substudy (NCT01245959). BMC Med. 2019;17:190.

    Article  Google Scholar 

  10. Zhang B, Tian J, Dong D, Gu D, Dong Y, Zhang L, et al. Radiomics features of multiparametric MRI as novel prognostic factors in advanced nasopharyngeal carcinoma. Clin Cancer Res. 2017;23:4259–69.

    Article  Google Scholar 

  11. Zhang F, Zhong L-Z, Zhao X, Dong D, Yao J-J, Wang S-Y, et al. A deep-learning-based prognostic nomogram integrating microscopic digital pathology and macroscopic magnetic resonance images in nasopharyngeal carcinoma: a multi-cohort study. Ther Adv Med Oncol. 2020;12:1758835920971416.

    CAS  PubMed  PubMed Central  Google Scholar 

  12. Zhang L, Zhou H, Gu D, Tian J, Zhang B, Dong D, et al. Radiomic nomogram: pretreatment evaluation of local recurrence in nasopharyngeal carcinoma based on MR imaging. J Cancer. 2019;10:4217–25.

    Article  Google Scholar 

  13. Peng H, Dong D, Fang M-J, Li L, Tang L-L, Chen L, et al. Prognostic value of deep learning PET/CT-based radiomics: potential role for future individual induction chemotherapy in advanced nasopharyngeal carcinoma. Clin Cancer Res. 2019;25:4271–9.

    Article  CAS  Google Scholar 

  14. Zhong L, Dong D, Fang X, Zhang F, Zhang N, Zhang L, et al. A deep learning-based radiomic nomogram for prognosis and treatment decision in advanced nasopharyngeal carcinoma: a multicentre study. EBioMedicine. 2021;70:103522.

    Article  Google Scholar 

  15. Kirienko M, Sollini M, Corbetta M, Voulaz E, Gozzi N, Interlenghi M, et al. Radiomics and gene expression profile to characterise the disease and predict outcome in patients with lung cancer. Eur J Nucl Med Mol Imaging. 2021;48:3643–55.

    Article  CAS  Google Scholar 

  16. Zinn PO, Singh SK, Kotrotsou A, Hassan I, Thomas G, Luedi MM, et al. A coclinical radiogenomic validation study: conserved magnetic resonance radiomic appearance of periostin-expressing glioblastoma in patients and xenograft models. Clin Cancer Res. 2018;24:6288–99.

    Article  CAS  Google Scholar 

  17. Fan M, Xia P, Clarke R, Wang Y, Li L. Radiogenomic signatures reveal multiscale intratumour heterogeneity associated with biological functions and survival in breast cancer. Nat Commun. 2020;11:4861.

    Article  CAS  Google Scholar 

  18. Dong D, Fang M-J, Tang L, Shan X-H, Gao J-B, Giganti F, et al. Deep learning radiomic nomogram can predict the number of lymph node metastasis in locally advanced gastric cancer: an international multicenter study. Ann Oncol. 2020;31:912–20.

    Article  CAS  Google Scholar 

  19. Dong D, Tang L, Li Z-Y, Fang M-J, Gao J-B, Shan X-H, et al. Development and validation of an individualized nomogram to identify occult peritoneal metastasis in patients with advanced gastric cancer. Ann Oncol. 2019;30:431–8.

    Article  CAS  Google Scholar 

  20. Wen Y-H, Wen W-P, Chen H-X, Li J, Zeng Y-H, Xu G. Endoscopic nasopharyngectomy for salvage in nasopharyngeal carcinoma: a novel anatomic orientation. Laryngoscope. 2010;120:1298–302.

    Article  Google Scholar 

  21. Tan M, Le QV. Efficientnet: rethinking model scaling for convolutional neural networks. In: ICML. 2019. pp. 6105–14.

  22. Katzman JL, Shaham U, Cloninger A, Bates J, Jiang T, Kluger Y. DeepSurv: personalized treatment recommender system using a Cox proportional hazards deep neural network. BMC Med Res Methodol. 2018;18:24.

    Article  Google Scholar 

  23. Friemel J, Rechsteiner M, Frick L, Böhm F, Struckmann K, Egger M, et al. Intratumor heterogeneity in hepatocellular carcinoma. Clin Cancer Res. 2015;21:1951–61.

    Article  CAS  Google Scholar 

  24. Yang F, Wang Y, Li Q, Cao L, Sun Z, Jin J, et al. Intratumor heterogeneity predicts metastasis of triple-negative breast cancer. Carcinogenesis. 2017;38:900–9.

    Article  CAS  Google Scholar 

  25. Huang Y-Q, Liang C-H, He L, Tian J, Liang C-S, Chen X, et al. Development and validation of a radiomics nomogram for preoperative prediction of lymph node metastasis in colorectal cancer. J Clin Oncol. 2016;34:2157–64.

    Article  Google Scholar 

  26. Leung S, Zee B, Ma BB, Hui EP, Mo F, Lai M, et al. Plasma Epstein-Barr viral deoxyribonucleic acid quantitation complements tumor-node-metastasis staging prognostication in nasopharyngeal carcinoma. J Clin Oncol. 2006;24:5414–8.

    Article  CAS  Google Scholar 

  27. Tang L-Q, Chen Q-Y, Fan W, Liu H, Zhang L, Guo L, et al. Prospective study of tailoring whole-body dual-modality [18F]fluorodeoxyglucose positron emission tomography/computed tomography with plasma Epstein-Barr virus DNA for detecting distant metastasis in endemic nasopharyngeal carcinoma at initial staging. J Clin Oncol. 2013;31:2861–9.

    Article  CAS  Google Scholar 

  28. Guo R, Tang L-L, Mao Y-P, Du X-J, Chen L, Zhang Z-C, et al. Proposed modifications and incorporation of plasma Epstein-Barr virus DNA improve the TNM staging system for Epstein-Barr virus-related nasopharyngeal carcinoma. Cancer. 2019;125:79–89.

    Article  CAS  Google Scholar 

  29. Leung S-F, Lo YMD, Chan ATC, To K-F, To E, Chan LYS, et al. Disparity of sensitivities in detection of radiation-naïve and postirradiation recurrent nasopharyngeal carcinoma of the undifferentiated type by quantitative analysis of circulating Epstein-Barr virus DNA1,2. Clin Cancer Res. 2003;9:3431–4.

    CAS  PubMed  Google Scholar 

  30. Nie P, Yang G, Wang N, Yan L, Miao W, Duan Y, et al. Additional value of metabolic parameters to PET/CT-based radiomics nomogram in predicting lymphovascular invasion and outcome in lung adenocarcinoma. Eur J Nucl Med Mol Imaging. 2021;48:217–30.

    Article  Google Scholar 

Download references

Acknowledgements

The authors thank Prof. Xuebin Xie from KiangWu Hospital, Macau, China for his great help about the clinical part in this paper.

Funding

This work was supported by the Strategic Priority Research Program of Chinese Academy of Sciences (XDB38040200), National Key R&D Program of China (2017YFA0205200, 2017YFC0908500, 2017YFC1309003), National Natural Science Foundation of China (82022036, 91959130, 81971776, 81771924, 62027901, 81930053, 81425018, 81672868, 81802775, 82073003, 82002852, 82003267, 81803105), the Beijing Natural Science Foundation (L182061), the Youth Innovation Promotion Association CAS (Y2021049, 2017175), the Sci-Tech Project Foundation of Guangzhou City (201707020039), the Sun Yat-sen University Clinical Research 5010 Program (2016010, 201315, 2015021, 2017010, 2016013, 2019023), Innovative research team of high-level local universities in Shanghai (SSMU-ZLCX20180500), the Natural Science Foundation of Guangdong Province (2017A030312003, 2018A0303131004), the Natural Science Foundation of Guangdong Province for Distinguished Young Scholar (2018B030306001), the Health & Medical Collaborative Innovation Project of Guangzhou City (No. 201803040003), Pearl River S&T Nova Program of Guangzhou (201806010135), the Planned Science and Technology Project of Guangdong Province (2019B020230002), Natural Science Foundation of Guangdong Province (2017A030312003), the Key Youth Teacher Cultivating Program of Sun Yat-sen University (20ykzd24), and the Fundamental Research Funds for the Central Universities.

Author information

Author notes

  1. Xun Zhao, Yu-Jing Liang, Xu Zhang, Dong-Xiang Wen and Wei Fan contributed equally to this work.

Authors and Affiliations

  1. School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, People’s Republic of China

    Xun Zhao, Di Dong & Jie Tian

  2. CAS Key Laboratory of Molecular Imaging, Beijing Key Laboratory of Molecular Imaging, The State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, No. 95 Zhongguancun East Road, Hai Dian District, Beijing, People’s Republic of China

    Xun Zhao, Di Dong & Jie Tian

  3. Sun Yat-Sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangzhou, 510060, People’s Republic of China

    Yu-Jing Liang, Dong-Xiang Wen, Lin-Quan Tang & Hai-Qiang Mai

  4. Department of Nasopharyngeal Carcinoma, Sun Yat-Sen University Cancer Center, 651 Dongfeng Road East, Guangzhou, 510060, People’s Republic of China

    Yu-Jing Liang, Xu Zhang, Dong-Xiang Wen, Wei Fan, Lin-Quan Tang & Hai-Qiang Mai

  5. Department of Nuclear Medicine, Sun Yat-Sen University Cancer Center, Guangzhou, 510060, People’s Republic of China

    Xu Zhang & Wei Fan

Authors
  1. Xun Zhao
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yu-Jing Liang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Xu Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Dong-Xiang Wen
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Wei Fan
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Lin-Quan Tang
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Di Dong
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Jie Tian
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Hai-Qiang Mai
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding authors

Correspondence to Lin-Quan Tang, Di Dong, Jie Tian or Hai-Qiang Mai.

Ethics declarations

Ethics approval

All procedures performed in the study and involving human participants were carried out in accordance with the ethical standards of the institutional and/or national research committee and with the principles of the 1964 Declaration of Helsinki and its later amendments or comparable ethical standards.

Consent to participate

The ethical review board of each participating center approved this retrospective study and waived the requirement of informed consent.

Conflict of interest

The authors declare no competing interests.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

This article is part of the Topical Collection on Oncology—Head and Neck.

Supplementary Information

Below is the link to the electronic supplementary material.

Supplementary file1 (DOCX 32 KB)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Zhao, X., Liang, YJ., Zhang, X. et al. Deep learning signatures reveal multiscale intratumor heterogeneity associated with biological functions and survival in recurrent nasopharyngeal carcinoma. Eur J Nucl Med Mol Imaging 49, 2972–2982 (2022). https://doi.org/10.1007/s00259-022-05793-x

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00259-022-05793-x

Keywords

Search

Navigation