Photodiagnosis and photodynamic therapy · 2026
Patient-level hyperspectral state-space learning for melanoma pathology diagnosis.
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Zhao Qi, Lei Shengxuan, Tian Chongxuan, Li Wei
Аннотация
BACKGROUND: Histopathologic differentiation of cutaneous melanoma from pigmented nevus can be difficult when morphology is subtle. Microscopic hyperspectral imaging records tissue in contiguous narrow spectral bands across the visible and near-infrared range and may provide quantitative optical information beyond routine bright-field microscopy. We developed MelanoSpec-SSM, a patient-level spectral-spatial state-space algorithm for label-free hyperspectral pathology diagnosis.
METHODS: This secondary methodological analysis used frozen sections from 100 patients (50 melanoma and 50 pigmented nevus) imaged from 400 to 1000 nm in 125 bands. After white-dark reflectance calibration, tissue extraction, spectral denoising, and strictly patient-level partitioning, MelanoSpec-SSM was compared with spectral classifiers, convolutional baselines, and a spectral-spatial transformer. The algorithm combines spectral state-space encoding, spatial patch learning, gated fusion, and attention-based multiple-instance aggregation.
RESULTS: In patient-level cross-validation, MelanoSpec-SSM achieved an accuracy of 0.940, sensitivity of 0.940, specificity of 0.940, F1-score of 0.940, and an area under the receiver operating characteristic curve of 0.972. In the held-out test set, 19 of 20 patients were correctly classified, with an area under the curve of 0.980. Model saliency and wavelength-removal analyses consistently assigned high task-specific importance to the 500-675 nm interval, while complementary near-infrared differences were also observed.
CONCLUSIONS: MelanoSpec-SSM provides an interpretable patient-level framework for visible-near-infrared hyperspectral differentiation of melanoma and pigmented nevus. The findings are preliminary because they arise from a single-center cohort of 100 patients and require external, multi-scanner, and prospective validation before clinical use.
METHODS: This secondary methodological analysis used frozen sections from 100 patients (50 melanoma and 50 pigmented nevus) imaged from 400 to 1000 nm in 125 bands. After white-dark reflectance calibration, tissue extraction, spectral denoising, and strictly patient-level partitioning, MelanoSpec-SSM was compared with spectral classifiers, convolutional baselines, and a spectral-spatial transformer. The algorithm combines spectral state-space encoding, spatial patch learning, gated fusion, and attention-based multiple-instance aggregation.
RESULTS: In patient-level cross-validation, MelanoSpec-SSM achieved an accuracy of 0.940, sensitivity of 0.940, specificity of 0.940, F1-score of 0.940, and an area under the receiver operating characteristic curve of 0.972. In the held-out test set, 19 of 20 patients were correctly classified, with an area under the curve of 0.980. Model saliency and wavelength-removal analyses consistently assigned high task-specific importance to the 500-675 nm interval, while complementary near-infrared differences were also observed.
CONCLUSIONS: MelanoSpec-SSM provides an interpretable patient-level framework for visible-near-infrared hyperspectral differentiation of melanoma and pigmented nevus. The findings are preliminary because they arise from a single-center cohort of 100 patients and require external, multi-scanner, and prospective validation before clinical use.