Precise diagnosis of small cell and non-small cell lung cancer based on Raman spectroscopy

IF 2.6 3区 医学 Q2 ONCOLOGY
Wendong Sun , Liwei Liao , Ruochen Zhu , Yuexiang Wang , Keren Chen , Gang Hou , Shuo Chen
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引用次数: 0

Abstract

Lung cancer has been the second most prevalent cancer globally and the leading cause of cancer-related mortality. Due to the similar clinical symptoms but different treatment protocols for different subtypes of lung cancer, precise diagnosis between small cell lung cancer (SCLC) and non-small cell lung cancer (NSCLC) is of paramount importance for guiding treatment and improving outcomes. In this study, we explored an innovative diagnostic approach that combines Raman spectroscopy with partial least squares analysis and linear discriminant analysis (PLS-LDA) to identify the presence of lung cancer and differentiate its specific subtype. Based on biochemical analysis results, statistically significant differences between SCLC and NSCLC groups were observed in levels of amino acids, nucleic acids, and collagen. In addition, high diagnostic accuracy rates of up to 100 % for in vitro cultured cell samples and 93.75 % for ex-vivo tissue samples were achieved to differentiate SCLC and NSCLC. The proposed method offers great potential in the precise diagnosis of lung cancer with different histologic subtypes, benefiting timely and rational treatment for lung cancer patients.

Abstract Image

基于拉曼光谱的小细胞和非小细胞肺癌的精确诊断。
肺癌是全球第二大流行癌症,也是癌症相关死亡的主要原因。由于不同亚型肺癌的临床症状相似,但治疗方案不同,因此准确诊断小细胞肺癌(SCLC)和非小细胞肺癌(NSCLC)对于指导治疗和改善预后至关重要。在这项研究中,我们探索了一种创新的诊断方法,将拉曼光谱与偏最小二乘分析和线性判别分析(PLS-LDA)相结合,以识别肺癌的存在并区分其特定亚型。生化分析结果显示,SCLC组和NSCLC组在氨基酸、核酸和胶原蛋白水平上差异有统计学意义。此外,体外培养细胞样本的诊断准确率高达100%,离体组织样本的诊断准确率高达93.75%,用于区分SCLC和NSCLC。该方法对不同组织学亚型肺癌的精确诊断具有很大的潜力,有利于肺癌患者的及时合理治疗。
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来源期刊
CiteScore
5.80
自引率
24.20%
发文量
509
审稿时长
50 days
期刊介绍: Photodiagnosis and Photodynamic Therapy is an international journal for the dissemination of scientific knowledge and clinical developments of Photodiagnosis and Photodynamic Therapy in all medical specialties. The journal publishes original articles, review articles, case presentations, "how-to-do-it" articles, Letters to the Editor, short communications and relevant images with short descriptions. All submitted material is subject to a strict peer-review process.
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