ArXiv · 2026
Evaluating the predictive power of pump-probe imaging contrast of melanin for metastatic outcome: A 71-patient study.
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Grass David, Bao Emma, Fischer Martin C, Selim M Angelica, Beasley Georgia M, Warren Warren S
Аннотация
SIGNIFICANCE: Most melanoma deaths arise from metastatic spread, yet current staging imperfectly identifies which primary tumors will progress. Melanin structure is altered during malignant transformation, and pump-probe microscopy (PPM) measures melanin excited-state dynamics in standard biopsy sections, offering molecular contrast that is complementary to morphology-based assessment.
AIM: To determine whether PPM-derived melanin excited-state dynamics in primary cutaneous melanoma can predict metastatic outcome.
APPROACH: We imaged unstained sections from 71 primary cutaneous melanomas and 18 melanoma metastases. Transient absorption curves were fit to a biexponential excited-state model, and the fit parameters were used as features for learning classifiers to predict metastatic outcome.
RESULTS: Melanin dynamics separated primary from metastatic tissue: both excited-state lifetimes were shorter in primaries, with common-language effect sizes up to 0.26. The same parameters did not separate primary tumors by outcome (effect sizes 0.41-0.61), and univariate logistic regression found no significant association for either the excited-state lifetimes or the ESA/GSB ratio. The best classifier reached 74% patient-level accuracy (AUC 0.73), a plateau reached by several unrelated architectures.
CONCLUSIONS: PPM robustly distinguishes primary from metastatic melanoma tissue, consistent with progressive melanin disaggregation. While it is clear that pump-probe features of melanin thus correlate with tissue differences, melanin dynamics in single sections of the primary tumor, confounded by heterogeneity, do not give robust predictions of metastatic outcome.
AIM: To determine whether PPM-derived melanin excited-state dynamics in primary cutaneous melanoma can predict metastatic outcome.
APPROACH: We imaged unstained sections from 71 primary cutaneous melanomas and 18 melanoma metastases. Transient absorption curves were fit to a biexponential excited-state model, and the fit parameters were used as features for learning classifiers to predict metastatic outcome.
RESULTS: Melanin dynamics separated primary from metastatic tissue: both excited-state lifetimes were shorter in primaries, with common-language effect sizes up to 0.26. The same parameters did not separate primary tumors by outcome (effect sizes 0.41-0.61), and univariate logistic regression found no significant association for either the excited-state lifetimes or the ESA/GSB ratio. The best classifier reached 74% patient-level accuracy (AUC 0.73), a plateau reached by several unrelated architectures.
CONCLUSIONS: PPM robustly distinguishes primary from metastatic melanoma tissue, consistent with progressive melanin disaggregation. While it is clear that pump-probe features of melanin thus correlate with tissue differences, melanin dynamics in single sections of the primary tumor, confounded by heterogeneity, do not give robust predictions of metastatic outcome.