{"title":"Plasma-based near-infrared spectroscopy for early diagnosis of lung cancer","authors":"","doi":"10.1016/j.jpba.2024.116376","DOIUrl":null,"url":null,"abstract":"<div><p>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.</p></div>","PeriodicalId":16685,"journal":{"name":"Journal of pharmaceutical and biomedical analysis","volume":null,"pages":null},"PeriodicalIF":3.1000,"publicationDate":"2024-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of pharmaceutical and biomedical analysis","FirstCategoryId":"3","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0731708524004163","RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"CHEMISTRY, ANALYTICAL","Score":null,"Total":0}
引用次数: 0
Abstract
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.
期刊介绍:
This journal is an international medium directed towards the needs of academic, clinical, government and industrial analysis by publishing original research reports and critical reviews on pharmaceutical and biomedical analysis. It covers the interdisciplinary aspects of analysis in the pharmaceutical, biomedical and clinical sciences, including developments in analytical methodology, instrumentation, computation and interpretation. Submissions on novel applications focusing on drug purity and stability studies, pharmacokinetics, therapeutic monitoring, metabolic profiling; drug-related aspects of analytical biochemistry and forensic toxicology; quality assurance in the pharmaceutical industry are also welcome.
Studies from areas of well established and poorly selective methods, such as UV-VIS spectrophotometry (including derivative and multi-wavelength measurements), basic electroanalytical (potentiometric, polarographic and voltammetric) methods, fluorimetry, flow-injection analysis, etc. are accepted for publication in exceptional cases only, if a unique and substantial advantage over presently known systems is demonstrated. The same applies to the assay of simple drug formulations by any kind of methods and the determination of drugs in biological samples based merely on spiked samples. Drug purity/stability studies should contain information on the structure elucidation of the impurities/degradants.