Deep Learning on Hyperspectral Pathology for Recurrence Prediction in Clear Cell Renal Cell Carcinoma
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Published on: June 18, 2020
Xulei Wang1, Wenshi Tian1,2, Yihan Zhao1
1Department of Urology, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, Shandong Institute of Nephrology, Jinan, China.
Background:
Clear cell renal cell carcinoma (ccRCC), the most common aggressive renal cancer subtype, shows marked heterogeneity that hinders recurrence prediction.
Objective:
To evaluate hyperspectral pathology imaging (HSI) with deep learning for individualized recurrence risk prediction in ccRCC.
Methods:
Slides from 48 patients with ccRCC were imaged using a 400-1000 nm hyperspectral microscope. Spectral data were preprocessed, and a dual-branch network (HSI-FusionNet) extracted spatial and spectral features via 2D and 1D convolution, followed by gated fusion and multiple instance learning (MIL) for patient-level prediction.
Results:
HSI-FusionNet achieved strong test performance (area under the receiver operating characteristic curve [AUC] = 0.912; sensitivity = 0.881; specificity = 0.846), outperforming ResNet-50, 1D-Convolutional Neural Network (CNN), and a 1D-Transformer. Recurrence-related spectral bands (530-580 and 830-900 nm) reflected hemoglobin and lipid-collagen differences.
Conclusion:
HSI with deep learning accurately identifies recurrent ccRCC and reveals molecular-metabolic signatures, supporting precision postoperative risk stratification.
