用光学相干断层扫描和拉曼光谱双峰系统在体内鉴别皮肤黑色素瘤和良性痣。

IF 2.3
Di Wu, Anatoly Fedorov Kukk, Rüdiger Panzer, Steffen Emmert, Bernhard Roth
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引用次数: 0

摘要

我们开发了一种多模态方法,包括使用OCT的光学成像和使用拉曼光谱的分子检测,以探索其在黑色素瘤皮肤癌和良性皮肤病变之间无创鉴别的能力。通过指数拟合提取的衰减系数、R2和RMSE等OCT关键参数被纳入机器学习,10倍交叉验证的准确率达到96.9%,AUC-ROC为0.99。拉曼光谱揭示了黑色素瘤和痣之间类胡萝卜素、酰胺i和CH2-CH3结构的差异,支持了OCT的发现。自身荧光背景强度变化进一步区分病变类型和增强病变评估。未来的工作将包括对更大的患者群体的调查,并将两种数据集结合在一个组合算法中。此外,将这两种模式和开发的方法与光声断层扫描和高频超声相结合,似乎有利于实现黑色素瘤皮肤癌的光学活检和改进诊断。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
In Vivo Differentiation of Cutaneous Melanoma From Benign Nevi With Dual-Modal System of Optical Coherence Tomography and Raman Spectroscopy.

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.

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