Pump-probe imaging of pigmented cutaneous melanoma primary lesions gives insight into metastatic potential.

Journal Article

Metastatic melanoma is associated with a poor prognosis, but no method reliably predicts which melanomas of a given stage will ultimately metastasize and which will not. While sentinel lymph node biopsy (SLNB) has emerged as the most powerful predictor of metastatic disease, the majority of people dying from metastatic melanoma still have a negative SLNB. Here we analyze pump-probe microscopy images of thin biopsy slides of primary melanomas to assess their metastatic potential. Pump-probe microscopy reveals detailed chemical information of melanin with subcellular spatial resolution. Quantification of the molecular signatures without reference standards is achieved using a geometrical representation of principal component analysis. Melanin structure is analyzed in unison with the chemical information by applying principles of mathematical morphology. Results show that melanin in metastatic primary lesions has lower chemical diversity than non-metastatic primary lesions, and contains two distinct phenotypes that are indicative of aggressive disease. Further, the mathematical morphology analysis reveals melanin in metastatic primary lesions has a distinct "dusty" quality. Finally, a statistical analysis shows that the combination of the chemical information with spatial structures predicts metastatic potential with much better sensitivity than SLNB and high specificity, suggesting pump-probe microscopy can be an important tool to help predict the metastatic potential of melanomas.

Full Text

Duke Authors

Cited Authors

  • Robles, FE; Deb, S; Wilson, JW; Gainey, CS; Selim, MA; Mosca, PJ; Tyler, DS; Fischer, MC; Warren, WS

Published Date

  • September 2015

Published In

Volume / Issue

  • 6 / 9

Start / End Page

  • 3631 - 3645

PubMed ID

  • 26417529

Electronic International Standard Serial Number (EISSN)

  • 2156-7085

International Standard Serial Number (ISSN)

  • 2156-7085

Digital Object Identifier (DOI)

  • 10.1364/boe.6.003631

Language

  • eng