A machine learning-driven Raman spectroscopy approach for non-invasive diagnosis of non-puerperal mastitis.

Journal: Analytical and bioanalytical chemistry
Published Date: Jan 16, 2026

Abstract

Early and rapid diagnosis of non-puerperal mastitis (NPM), as well as elucidation of its specific pathological features, is of important clinical and scientific value. Peripheral blood mononuclear cells (PBMCs), which are key mediators in the inflammatory response, contribute substantially to disease onset, progression, and therapeutic effect, making them promising biomarkers for the early identification and management of inflammatory processes. Nevertheless, novel approaches for the detection and analysis of PBMCs remain urgently needed to facilitate the development of liquid biopsy strategies. In this study, we employed Raman spectroscopy to characterize molecular alterations in PBMCs derived from two distinct groups of NPM patients and healthy controls. Additionally, several machine learning algorithms, including principal component analysis (PCA), linear discriminant analysis (LDA), partial least squares discriminant analysis (PLSDA), and support vector machine (SVM), were applied to establish diagnostic prediction models for NPM, yielding area under the curve (AUC) values exceeding 0.93. Our findings indicate that PBMC-based liquid biopsy coupled with Raman spectroscopy and machine learning provides novel opportunities for the diagnosis of NPM.

Authors

  • Yongqi Li
    Department of Breast Surgery, The Second Hospital of Shandong University, Jinan, Shandong 250033, China.
  • Haoran Zhang
    Massachusetts Institute of Technology, Cambridge, MA, USA.
  • Yining Jia
    Department of Breast Surgery, The Second Hospital of Shandong University, Jinan, Shandong 250033, China.
  • Chao Wang
    College of Agriculture, Shanxi Agricultural University, Taigu, Shanxi, China.
  • Fei Zhou
    College of Information Science and Engineering, Xinjiang University, Urumqi 830046, China.
  • Ying Shan
    School of Electronic Engineering, Beijing University of Posts and Telecommunications, Beijing, China.
  • Dong-Xu Liu
    Breast Center, The Second Qilu Hospital of Shandong University, 247 Beiyuan St, Jinan, Shandong Province, 250033, People's Republic of China.
  • Zhigang Yu
    Department of Biomedical Engineering, ShenZhen University, ShenZhen, 518000, China.
  • Chao Zheng
    School of Biomedical Engineering, Southern Medical University, Guangzhou, Guangdong 510515 People's Republic of China.

Keywords

No keywords available for this article.

External Resources

Popular Topics
Recent Journals