Diagnostic Study of Head and Neck Metastatic Tumors From Different Primary Sites Based on Stacking Machine Learning Methods.

Journal: Journal of biophotonics
Published Date: Jun 24, 2025

Abstract

Metastatic tumors of the head and neck (MTHN) typically indicate advanced disease with a poor prognosis, originating from cells that spread from other body parts. Diagnosis generally relies on slow and error-prone methods like imaging and histopathology. Addressing the need for a faster, more accurate diagnostic method, this study uses hyperspectral imaging to gather detailed cellular data from 208 patients at six primary MTHN sites. Techniques select characteristic spectral bands, and models including SVM, LightGBM, and ResNet are developed. A high-performance classification model, MTHN-SC, employs stacking technology with SVM and LightGBM as base learners and Random Forest as the meta-learner, achieving a diagnostic accuracy of 82.47%, outperforming other models. This research enhances targeted treatment strategies and advances the application of hyperspectral technology in identifying MTHN primary sites.

Authors

  • Yifei Liu
    College of Data Science, Taiyuan University of Technology, Taiyuan, Shanxi, China.
  • Cong Wu
    Department of Dermatology, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, Shaanxi, China.
  • Junpeng Ma
    Shandong Inspur Intelligent Medical Technology Co. Ltd, Jinan, China.
  • Liang Ma
    College of Information and Management, National University of Defense Technology, Changsha 410073, China.
  • Chongxuan Tian
    School of Control Science and Engineering, Shandong University, Jinan, Shandong, 250061, China.
  • Yunze Li
    School of Control Science and Engineering, Shandong University, Qianfoshan Campus, 17923 Jingshi Road, Jinan, Shandong 250061, China.
  • Jinlin Deng
    School of Control Science and Engineering, Shandong University, Qianfoshan Campus, 17923 Jingshi Road, Jinan, Shandong 250061, China.
  • Qize Lv
    School of Control Science and Engineering, Shandong University, Qianfoshan Campus, 17923 Jingshi Road, Jinan, Shandong 250061, China.
  • Wei Li
    Department of Nephrology, The Second Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, China.
  • Miaoqing Zhao
    Department of Pathology, Shandong Provincial Hospital affiliated to Shandong University, Shandong University, Jinan, Shandong, P.R. China.

Keywords

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