Abstract
A multimodal method comprising optical imaging using OCT and molecular detection using Raman spectroscopy was developed to explore its capability for noninvasive differentiation between melanoma skin cancer and benign skin lesions. Key OCT parameters like the attenuation coefficient, R2, and RMSE, extracted through exponential fitting, were incorporated into machine learning, achieving 96.9% accuracy and an AUC-ROC of 0.99 in 10-fold cross-validation. Raman spectroscopy revealed differences in carotenoid, amide-I, and CH2–CH3 structures between melanoma and nevi, supporting the OCT findings. Autofluorescence background intensity variations further distinguished lesion types and enhanced lesion assessment. Future work will include the investigation of larger patient groups and the combination of both data sets in a combined algorithm. Also, the integration of both modalities and the developed method with photoacoustic tomography and high-frequency ultrasound appears beneficial toward achieving an optical biopsy of melanoma skin cancer and improving diagnostics.
| Original language | English |
|---|---|
| Article number | e70040 |
| Number of pages | 10 |
| Journal | Journal of biophotonics |
| Volume | 18 |
| Issue number | 9 |
| DOIs | |
| Publication status | Published - 4 Sept 2025 |
UN Sustainable Development Goals (SDGs)
This output contributes to the following UN Sustainable Development Goals (SDGs)
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SDG 3 Good Health and Well-being
Keywords
- attenuation coefficient
- melanoma diagnosis
- multimodal noninvasive cancer diagnosis
- optical coherence tomography
- Raman spectroscopy
ASJC Scopus subject areas
- General Chemistry
- General Materials Science
- General Biochemistry,Genetics and Molecular Biology
- General Engineering
- General Physics and Astronomy
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