Finding reduced Raman spectroscopy fingerprint of skin samples for melanoma diagnosis through machine learning.

Abstract

Early-stage detection of cutaneous melanoma can vastly increase the chances of cure. Excision biopsy followed by histological examination is considered the gold standard for diagnosing the disease, but requires  long highcost processing time, and may be biased, as it involves qualitative assessment by a professional. In this paper, we present a new machine learning approach using raw data for skin Raman spectra as  input. The approach is highly efficient for classifying benign versus malignant skin lesions (AUC 0.98, 95% CI 0.97–0.99). Furthermore, we present a high-performance model (AUC 0.97, 95% CI 0.95–0.98) using  a miniaturized spectral range (896–1039 cm􀀀 1), thus demonstrating that only a single fragment of the biological fingerprint Raman region is needed for producing an accurate diagnosis. These findings could  favor the future development of a cheaper and dedicated Raman spectrometer for fast and accurate cancer diagnosis.Early-stage detection of cutaneous melanoma can vastly increase the chances of cure. Excision  biopsy followed by histological examination is considered the gold standard for diagnosing the disease, but requires long highcost processing time, and may be biased, as it involves qualitative assessment by a  professional. In this paper, we present a new machine learning approach using raw data for skin Raman spectra as input. The approach is highly efficient for classifying benign versus malignant skin lesions (AUC  0.98, 95% CI 0.97–0.99). Furthermore, we present a high-performance model (AUC 0.97, 95% CI 0.95–0.98) using a miniaturized spectral range (896–1039 cm􀀀 1), thus demonstrating that only a single  fragment of the biological fingerprint Raman region is needed for producing an accurate diagnosis. These findings could favor the future development of a cheaper and dedicated Raman spectrometer for fast and  accurate cancer diagnosis.

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