Photodiagnosis and photodynamic therapy · 2026
Label-Free Microscopic Hyperspectral Imaging for Papillary Thyroid Carcinoma Photodiagnosis Using Patient-Level Spectral-Spatial Deep Learning.
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Sun Qingying, Meng Feifei, Shi Guangxi, Tian Wenshi, Wang Xulei, Yan Jiazheng, Ma Zhe
Аннотация
BACKGROUND: Papillary thyroid carcinoma (PTC) may overlap microscopically with Hashimoto thyroiditis (HT) and nodular goiter (NG). Label-free optical assessment that captures morphology and wavelength-dependent tissue contrast could provide an objective adjunct to conventional pathology.
METHODS: We retrospectively analyzed 180 patients (72 PTC, 54 HT, and 54 NG). Unstained serial sections were imaged by visible-near-infrared microscopic hyperspectral imaging (400-1000 nm; effective range 420-950 nm), with adjacent hematoxylin-eosin sections as the reference. We developed Thyroid Hyperspectral Imaging Multiple-Instance Learning (ThyroHSI-MIL), a patient-level dual-branch framework combining multi-scale spectral encoding, wavelength attention, spatial feature extraction, gated spectral-spatial fusion, and attention-based multiple-instance aggregation. After white-dark calibration, Savitzky-Golay smoothing, and standard normal variate normalization, model performance was assessed by stratified five-fold patient-level cross-validation.
RESULTS: For PTC versus benign lesions, ThyroHSI-MIL achieved an AUC of 0.927, accuracy of 0.856, sensitivity of 0.875, specificity of 0.843, and F1 score of 0.842. Three-class classification achieved a macro-average AUC of 0.903 and accuracy of 0.844. The strongest wavelength-attention peaks occurred near 468 and 530 nm. Ablation analysis supported contributions from the spectral and spatial branches, wavelength attention, gated fusion, and patient-level aggregation.
CONCLUSIONS: Label-free microscopic hyperspectral imaging with patient-level spectral-spatial learning accurately differentiated PTC from benign thyroid lesions and provided wavelength- and region-level interpretability. External and prospective validation is warranted before clinical deployment.
METHODS: We retrospectively analyzed 180 patients (72 PTC, 54 HT, and 54 NG). Unstained serial sections were imaged by visible-near-infrared microscopic hyperspectral imaging (400-1000 nm; effective range 420-950 nm), with adjacent hematoxylin-eosin sections as the reference. We developed Thyroid Hyperspectral Imaging Multiple-Instance Learning (ThyroHSI-MIL), a patient-level dual-branch framework combining multi-scale spectral encoding, wavelength attention, spatial feature extraction, gated spectral-spatial fusion, and attention-based multiple-instance aggregation. After white-dark calibration, Savitzky-Golay smoothing, and standard normal variate normalization, model performance was assessed by stratified five-fold patient-level cross-validation.
RESULTS: For PTC versus benign lesions, ThyroHSI-MIL achieved an AUC of 0.927, accuracy of 0.856, sensitivity of 0.875, specificity of 0.843, and F1 score of 0.842. Three-class classification achieved a macro-average AUC of 0.903 and accuracy of 0.844. The strongest wavelength-attention peaks occurred near 468 and 530 nm. Ablation analysis supported contributions from the spectral and spatial branches, wavelength attention, gated fusion, and patient-level aggregation.
CONCLUSIONS: Label-free microscopic hyperspectral imaging with patient-level spectral-spatial learning accurately differentiated PTC from benign thyroid lesions and provided wavelength- and region-level interpretability. External and prospective validation is warranted before clinical deployment.