肺癌(LC)仍然是中国的主要死亡原因,主要是由于晚期诊断。本研究旨在评估使用基于等离子体的近红外光谱(NIRS)进行LC早期诊断的有效性。共收集了171份血浆样本,包括73名健康对照(HC),73LC,和25个肺良性肿瘤(B)。利用NIRS测量样品的光谱。预处理方法,包括定心和缩放,标准正态变量,乘法散射校正,Savitzky-Golay平滑,Savitzky-Golay一阶导数,并应用基线校正。随后,4种机器学习(ML)算法,包括偏最小二乘(PLS),支持向量机(SVM),梯度增压机,和随机森林,用于使用训练集数据开发诊断模型。然后,使用测试集样本评估每个模型的预测性能.该研究进行了如下5个比较:LC和HC,LC和B,B和HC,患病组(D)和HC,还有LC,B和HC。在5个比较中,SVM通过一定的预处理方法始终产生最佳性能,在LC和HC的比较中实现1.0(κ:1.0)的总体准确度,B和HC,以及D和HC。预处理被认为是开发ML模型的关键步骤。有趣的是,PLS在5个比较中表现出显著的稳定性和相对较高的预测性能,即使它没有达到像SVM那样的最高结果。然而,这些算法都不能有效区分B和LC。这些发现表明,基于等离子体的NIRS与ML算法的结合是一种快速,非侵入性,有效,LC早期诊断的经济方法。
Lung cancer (LC) continues to be a leading death cause in
China, primarily due to late diagnosis. This study aimed to evaluate the effectiveness of using plasma-based near-infrared spectroscopy (NIRS) for LC early diagnosis. A total of 171 plasma samples were collected, including 73 healthy controls (HC), 73 LC, and 25 benign lung tumors (B). NIRS was utilized to measure the spectra of samples. Pre-processing methods, including centering and scaling, standard normal variate, multiplicative scatter correction, Savitzky-Golay smoothing, Savitzky-Golay first derivative, and baseline correction were applied. Subsequently, 4 machine learning (ML) algorithms, including partial least squares (PLS), support vector machines (SVM), gradient boosting machine, and random forest, were utilized to develop diagnostic models using train set data. Then, the predictive performance of each model was evaluated using test set samples. The study was conducted in 5 comparisons as follows: LC and HC, LC and B, B and HC, the diseased group (D) and HC, as well as LC, B and HC. Among the 5 comparisons, SVM consistently generated the best performance with a certain pre-processing method, achieving overall accuracy of 1.0 (kappa: 1.0) in the comparisons of LC and HC, B and HC, as well as D and HC. Pre-processing was identified as a crucial step in developing ML models. Interestingly, PLS demonstrated remarkable stability and relatively high predictive performance across the 5 comparisons, even though it did not achieve the top results like SVM. However, none of these algorithms were able to effectively distinguish B from LC. These findings indicate that the combination of plasma-based NIRS with ML algorithms is a rapid, non-invasive, effective, and economical method for LC early diagnosis.