拉曼光谱
结直肠癌
癌症
化学
主成分分析
病理
谱线
材料科学
分析化学(期刊)
核磁共振
医学
计算机科学
内科学
光学
色谱法
物理
人工智能
天文
作者
Maria Karnachoriti,Ioannis Stathopoulos,M. Kouri,E. Spyratou,Spyros Orfanoudakis,Dimitrios Lykidis,Maria Lambropoulou,Nikolaos Danias,N. Arkadopoulos,Efstathios Efstathopoulos,Y. S. Raptis,Ioannis Seimenis,Athanassios G. Kontos
标识
DOI:10.1016/j.saa.2023.122852
摘要
Human colorectal tissues obtained by ten cancer patients have been examined by multiple micro-Raman spectroscopic measurements in the 500-3200 cm-1 range under 785 nm excitation. Distinct spectral profiles are recorded from different spots on the samples: a predominant 'typical' profile of colorectal tissue, as well as those from tissue topologies with high lipid, blood or collagen content. Principal component analysis identified several Raman bands of amino acids, proteins and lipids which allow the efficient discrimination of normal from cancer tissues, the first presenting plurality of Raman spectral profiles while the last showing off quite uniform spectroscopic characteristics. Tree-based machine learning experiment was further applied on all data as well as on filtered data keeping only those spectra which characterize the largely inseparable data clusters of 'typical' and 'collagen-rich' spectra. This purposive sampling evidences statistically the most significant spectroscopic features regarding the correct identification of cancer tissues and allows matching spectroscopic results with the biochemical changes induced in the malignant tissues.
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