激光诱导击穿光谱
阿达布思
黑色素瘤
Boosting(机器学习)
医学
内科学
人工智能
肿瘤科
支持向量机
激光器
计算机科学
癌症研究
物理
光学
作者
Zhifang Zhao,Xiangjun Xu,Mengyu Bao,Yongyue Zheng,Tianzhong Luo,Bingheng Lu,Geer Teng,Qianqian Wang,Muhammad Nouman Khan,Jun Yong
标识
DOI:10.1016/j.microc.2024.110955
摘要
For melanoma, early screening could increase the cure rate, while staging helps to make treatment strategies. Blood sampling has advantages of little damage, convenient operation and low cost, which combined with laser-induced breakdown spectroscopy (LIBS) has been utilized for tumor diagnoses. Here, we proposed to accurately attain early screening and staging of melanoma blood using LIBS. The serum was collected from 25 melanoma mice and 10 healthy controls on the 7th, 14th, 21st and 28th days. Compared with k nearest neighbor (kNN), support vector machine (SVM) and back propagation neural network (BPNN) models, the adaptive boosting of BPNN (BP_AdaBoost) models had the best accuracies of 83.37 % for early screening and 96.18 % for staging, respectively. Using mutual information (MI) method to select features, the accuracies of BP_AdaBoost models were improved to 86.11 % for early screening and 96.91 % for staging, respectively. Besides, the difference significance of elements and molecular bands in the serum was examined by the Kruskal-Wallis (K-W) test. The test results showed that obvious differences of Ca and Na existed in both early screening and staging, while K and Mg made significant differences in staging, consistent with roles of Ca and Na in the whole process of tumor development and roles of K and Mg in tumor proliferation and metastasis. Overall, all results demonstrated that early screening and staging of melanoma could be accurately realized using blood based on LIBS.
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