Dual energy CT image prediction on primary tumor of lung cancer for nodal metastasis using deep learning.

Journal: Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Published Date: May 15, 2021

Abstract

Lymph node metastasis (LNM) identification is the most clinically important tasks related to survival and recurrence from lung cancer. However, the preoperative prediction of nodal metastasis remains a challenge to determine surgical plans and pretreatment decisions in patients with cancers. We proposed a novel deep prediction method with a size-related damper block for nodal metastasis (Nmet) identification from the primary tumor in lung cancer generated by gemstone spectral imaging (GSI) dual-energy computer tomography (CT). The best model is the proposed method trained by the 40 keV dataset achieves an accuracy of 86 % and a Kappa value of 72 % for Nmet prediction. In the experiment, we have 11 different monochromatic images from 40∼140 keV (the interval is 10 keV) for each patient. When we used the model of 40 keV dataset, there has significant difference in other energy levels (unit of keV). Therefore, we apply in 5-fold cross-validation to explain the lower keV is more efficient to predict Nmet of the primary tumor. The result shows that tumor heterogeneity and size contributed to the proposed model to estimate whether absence or presence of nodal metastasis from the primary tumor.

Authors

  • You-Wei Wang
    Department of Computer Science and Information Engineering, National Taiwan University, Taipei, Taiwan.
  • Chii-Jen Chen
    Department of Medical Imaging and Radiological Technology, Yuanpei University of Medical Technology, Hsinchu, Taiwan. Electronic address: cjchen@mail.ypu.edu.tw.
  • Hsu-Cheng Huang
    Department of Medical Imaging, National Taiwan University Hospital and National Taiwan University College of Medicine, Taipei, Taiwan; Department of Radiology, Taipei City Hospital, Yangming Branch, Taipei, Taiwan.
  • Teh-Chen Wang
    Department of Medical Imaging, Taipei City Hospital, Yangming Branch, Taipei, Taiwan.
  • Hsin-Ming Chen
    Department of Medical Imaging, National Taiwan University Hospital and National Taiwan University College of Medicine, Taipei, Taiwan.
  • Jin-Yuan Shih
    Department of Internal Medicine, National Taiwan University Hospital and National Taiwan University College of Medicine, Taipei, Taiwan.
  • Jin-Shing Chen
    Department of Surgery, National Taiwan University Hospital and National Taiwan University College of Medicine, Taipei, Taiwan.
  • Yu-Sen Huang
    Department of Medical Imaging, National Taiwan University Hospital and National Taiwan University College of Medicine, Taipei, Taiwan.
  • Yeun-Chung Chang
    Department of Medical Imaging, National Taiwan University Hospital and National Taiwan University College of Medicine, Taipei, Taiwan.
  • Ruey-Feng Chang
    Graduate Institute of Biomedical Electronics and Bioinformatics, National Taiwan University, Taipei 10617, Taiwan and Department of Computer Science and Information Engineering, National Taiwan University, Taipei 10617, Taiwan.

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