Reliable lymph node metastasis prediction in head neck cancer through automated multi-objective model

Zhiguo Zhou, Michael Dohopolski, Liyuan Chen, Xi Chen, Steve Jiang, David Sher, Jing Wang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

Abstract

Lymph node metastasis (LNM) plays an important role for accurately diagnosing and treating the patients with head neck cancer. Positron emission tomography (PET) and computed tomography (CT) are two primary imaging modalities used for identifying LNM status. However, the uncertainty of LNM may exist especially for reactive or small nodes. Furthermore, identifying the LNM on PET or CT is greatly dependent on the physician's experience. Therefore, developing a reliable and automatic model is essential for accurately identifying LNM. Multi-objective models have shown promising predictive results by considering different objectives such as sensitivity and specificity. However, most multi-objective models need to choose an optimal model manually. In this work, we proposed an automated multi-objective learning model (AutoMO) for predicting LNM reliably. Instead of picking one optimal model, all the Pareto-optimal models with the calculated relative weights are used in AutoMO. Then the evidential reasoning (ER) approach is used for fusing the output probability for obtaining more reliable results than traditional fusion method. We built three models for PET, CT and PETCT and the results showed that PETCT outperformed two single modality based models. The comparative study demonstrated that AutoMO obtained better performance than current available multi-objective and deep learning methods, and more reliable results can be acquired when using ER fusion.

Original languageEnglish (US)
Title of host publication2019 IEEE EMBS International Conference on Biomedical and Health Informatics, BHI 2019 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728108483
DOIs
StatePublished - May 2019
Event2019 IEEE EMBS International Conference on Biomedical and Health Informatics, BHI 2019 - Chicago, United States
Duration: May 19 2019May 22 2019

Publication series

Name2019 IEEE EMBS International Conference on Biomedical and Health Informatics, BHI 2019 - Proceedings

Conference

Conference2019 IEEE EMBS International Conference on Biomedical and Health Informatics, BHI 2019
CountryUnited States
CityChicago
Period5/19/195/22/19

Keywords

  • Automated multi-objective learning (AutoMO)
  • Evidential reasoning
  • Head neck cancer
  • Lymph node metastasis
  • Multi-objective optimization

ASJC Scopus subject areas

  • Artificial Intelligence
  • Signal Processing
  • Information Systems and Management
  • Biomedical Engineering
  • Health Informatics
  • Radiology Nuclear Medicine and imaging

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  • Cite this

    Zhou, Z., Dohopolski, M., Chen, L., Chen, X., Jiang, S., Sher, D., & Wang, J. (2019). Reliable lymph node metastasis prediction in head neck cancer through automated multi-objective model. In 2019 IEEE EMBS International Conference on Biomedical and Health Informatics, BHI 2019 - Proceedings [8834658] (2019 IEEE EMBS International Conference on Biomedical and Health Informatics, BHI 2019 - Proceedings). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/BHI.2019.8834658