Computer Science Dept, Federal University of Minas Gerais, Brazil; Kunumi, Brazil.
Computer Science Dept, Federal University of Minas Gerais, Brazil.
Artif Intell Med. 2021 Oct;120:102161. doi: 10.1016/j.artmed.2021.102161. Epub 2021 Aug 28.
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 high-cost 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), 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.
早期皮肤黑色素瘤的检测可以大大提高治愈的机会。切除活检后进行组织学检查被认为是诊断该疾病的金标准，但需要长时间的高成本处理，并且可能存在偏差，因为它涉及到专业人员的定性评估。在本文中，我们提出了一种新的机器学习方法，使用皮肤拉曼光谱的原始数据作为输入。该方法在分类良性和恶性皮肤病变方面非常高效（AUC 0.98，95%CI 0.97-0.99）。此外，我们还提出了一种使用小型化光谱范围（896-1039 cm）的高性能模型（AUC 0.97，95%CI 0.95-0.98），这表明仅需生物指纹拉曼区域的单个片段即可进行准确诊断。这些发现可能有利于未来开发更便宜、专用的拉曼光谱仪，以实现快速、准确的癌症诊断。
