Objective:

Assess whether handheld Raman spectroscopy combined with machine learning can distinguish normal skin, basal cell carcinoma (BCC), and squamous cell carcinoma (SCC).

Approach:

• Measurement: Researchers used a mobile system with a 785-nm laser and handheld probe to analyze more than 50 removed tissue samples, generating almost 1,000 Raman spectra.
• Analysis: Several machine-learning methods were compared for classifying the spectra into normal skin, BCC, or SCC.

Key Findings:

• K-nearest neighbors and support vector machine models achieved approximately 84% overall accuracy; the support vector machine had 78.7% sensitivity and 88.6% specificity.
• A shallow neural network achieved 80.8% accuracy and the highest area under the ROC curve (0.910).
• Models distinguished normal skin from cancer more successfully than they distinguished BCC from SCC, whose molecular patterns overlapped.
• Cancer samples generally showed stronger protein-related signals, while normal skin showed stronger lipid-related signals.

Interpretation:

The study found that Raman spectra contain biochemical information that machine-learning models can use to classify skin tissue. It assessed analytical performance, not clinical diagnostic accuracy.

Limitations:

• Testing was performed on removed tissue rather than directly on patients.
• The sample set included just over 50 specimens.
• Reported performance remained below the greater-than-90% sensitivity, specificity, and accuracy the researchers considered desirable for clinical use.

Sources:

• Proceedings of SPIE study, as described in the supplied article context