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

You-Wei Wang; Chii-Jen Chen; Hsu-Cheng Huang; Teh-Chen Wang; Hsin-Ming Chen; Jin-Yuan Shih; Jin-Shing Chen; Yu-Sen Huang; Yeun-Chung Chang; Ruey-Feng Chang

doi:10.1016/j.compmedimag.2021.101935

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

Comput Med Imaging Graph. 2021 Jul:91:101935. doi: 10.1016/j.compmedimag.2021.101935. Epub 2021 May 15.

Authors

Affiliations

¹ Department of Computer Science and Information Engineering, National Taiwan University, Taipei, Taiwan.
² Department of Medical Imaging and Radiological Technology, Yuanpei University of Medical Technology, Hsinchu, Taiwan.
³ Department of Medical Imaging, Taipei City Hospital, Yangming Branch, Taipei, Taiwan.
⁴ Department of Medical Imaging, National Taiwan University Hospital and National Taiwan University College of Medicine, Taipei, Taiwan.
⁵ Department of Internal Medicine, National Taiwan University Hospital and National Taiwan University College of Medicine, Taipei, Taiwan.
⁶ Department of Surgery, National Taiwan University Hospital and National Taiwan University College of Medicine, Taipei, Taiwan.
⁷ Department of Medical Imaging, National Taiwan University Hospital and National Taiwan University College of Medicine, Taipei, Taiwan. Electronic address: [email protected].
⁸ Department of Computer Science and Information Engineering, National Taiwan University, Taipei, Taiwan. Electronic address: [email protected].

PMID: 34090261
DOI: 10.1016/j.compmedimag.2021.101935

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.

Keywords: Deep learning; Dual energy CT; Lymph node metastasis; Nodal metastasis.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Computers
Deep Learning*
Humans
Lung Neoplasms* / diagnostic imaging
Lymphatic Metastasis / diagnostic imaging
Tomography, X-Ray Computed