Hyperspectral Imaging Combined With Deep Learning for Precision Grading of Clear Cell Renal Cell Carcinoma.

Journal: Journal of biophotonics
Published Date: Jun 23, 2025

Abstract

This study presents an integrated approach combining hyperspectral imaging (HSI) and deep learning for accurate grading of clear cell renal cell carcinoma (ccRCC). A refined preprocessing pipeline-including wavelet-based denoising and principal component analysis (PCA)-effectively enhances image quality and reduces data dimensionality. The proposed architecture utilizes a 1D convolutional neural network with attention mechanisms and a Transformer module to extract both local spectral features and global contextual information. Evaluated on a dataset of 80 ccRCC samples, the model achieves 90.32% accuracy, 89.65% sensitivity, and 90.15% specificity, outperforming several state-of-the-art models. These findings demonstrate the potential of HSI-based deep learning systems to improve diagnostic accuracy and support more precise, personalized treatment planning in renal oncology.

Authors

  • Guoxia Zhang
    Department of Pathology, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, Jinan, Shangdong Province, China.
  • Jing Zhang
    MOEMIL Laboratory, School of Optoelectronic Information, University of Electronic Science and Technology of China, Chengdu, China.
  • Xulei Wang
    Department of Urology, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, Shandong Institute of Nephrology, Jinan, China.
  • Lv Haiyue
    Shandong University of Traditional Chinese Medicine, School of Acupuncture and Tuina, Jinan, China.
  • Mengqiu Zhang
    Shandong Center for Disease Control and Prevention Health Service Center, Jinan, China.
  • Chunlei Wang
    Analytical Sciences, BioPharmaceuticals R&D, AstraZeneca, Gaithersburg, Maryland 20878, United States.
  • Xiaoqing Yang
    Didi Chuxing, Beijing, China.

Keywords

No keywords available for this article.

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