Lung cancer staging is crucial for personalized treatment and improved prognosis. We propose a novel bimodal diagnostic approach that integrates LIBS and Raman technologies into a single platform, enabling comprehensive tissue elemental and molecular analysis. This strategy identifies critical staging elements and molecular marker signatures of lung tumors. LIBS detects concentration patterns of elemental lines including Mg (I), Mg (II), Ca (I), Ca (II), Fe (I), and Cu (II). Concurrently, Raman spectroscopy identifies changes in molecular content, such as phenylalanine (1033 cm), tyrosine (1174 cm), tryptophan (1207 cm), amide III (1267 cm), and proteins (1126 cm and 1447 cm), among others. The bimodal information is fused using a decision-level Bayesian fusion model, significantly enhancing the performance of the convolutional neural network architecture in classification algorithms, with an accuracy of 99.17 %, sensitivity of 99.17 %, and specificity of 99.88 %. This study provides a powerful new tool for the accurate staging and diagnosis of lung tumors.

中文翻译：

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基于激光诱导击穿和拉曼光谱的决策级数据融合：双峰光谱诊断不同阶段肺癌的研究


肺癌分期对于个性化治疗和改善预后至关重要。我们提出了一种新颖的双模诊断方法，将 LIBS 和拉曼技术集成到一个平台中，从而实现全面的组织元素和分子分析。该策略确定了肺部肿瘤的关键分期元素和分子标记特征。 LIBS 检测元素线的浓度模式，包括 Mg (I)、Mg (II)、Ca (I)、Ca (II)、Fe (I) 和 Cu (II)。同时，拉曼光谱可识别分子含量的变化，例如苯丙氨酸 (1033 cm)、酪氨酸 (1174 cm)、色氨酸 (1207 cm)、酰胺 III (1267 cm) 和蛋白质 (1126 cm 和 1447 cm) 等。使用决策级贝叶斯融合模型融合双峰信息，显着增强了卷积神经网络架构在分类算法中的性能，准确率达 99.17%，灵敏度达 99.17%，特异性达 99.88%。这项研究为肺部肿瘤的准确分期和诊断提供了强大的新工具。