Objective:

To evaluate the effectiveness of a handheld Raman spectroscopy system combined with machine learning in distinguishing normal skin, basal cell carcinoma (BCC), and squamous cell carcinoma (SCC).

Approach:

• Methodology: Researchers used a mobile Raman spectroscopy system with a 785-nm laser and handheld probe to analyze over 50 removed tissue samples, generating nearly 1,000 Raman spectra.
• Machine Learning Models: Various machine-learning methods were compared, including k-nearest neighbors, support vector machines, shallow neural networks, partial least squares discriminant analysis, and principal component analysis with quadratic discriminant analysis.

Key Findings:

• K-nearest neighbors and support vector machine models achieved approximately 84% accuracy.
• The support vector machine had a sensitivity of 78.7% and specificity of 88.6%.
• A shallow neural network achieved 80.8% accuracy with the highest area under the receiver operating characteristic curve at 0.910.
• Partial least squares discriminant analysis showed 95.5% specificity but only 66.5% sensitivity.
• Normal skin and cancerous tissues were more easily distinguished than BCC from SCC due to overlapping molecular patterns.

Interpretation:

Raman spectra contain biochemical information useful for classifying skin tissue, focusing on analytical performance rather than clinical diagnostic accuracy.

Limitations:

• Testing was conducted on removed tissue samples rather than directly on patients.
• The sample size was limited to just over 50 specimens.
• Reported performance was below the desired clinical thresholds of over 90% sensitivity, specificity, and accuracy.

Conclusion:

The study indicates potential for noninvasive tools in skin cancer diagnosis.

Sources:

• Proceedings of SPIE