Establishment and assessment of an early screening model for cervical cancer based on single-cell Raman spectroscopy combined with machine learning algorithms

• Published in: 精准医学杂志 Vol. 40; no. 4; pp. 348 - 352
• Main Author: MA Dongmei, ZHAO Wenjie, LIU Shihai, XU Haicang, CAI Duo, JI Yuetong, XU Jian, GUO Cancan, MA Bo, PAN Huazheng
• Format: Journal Article
• Language: Chinese
• Published: Editorial Office of Journal of Precision Medicine 01.08.2025
• Subjects: uterine cervical neoplasms|single-cell analysis|spectrum analysis, raman|machine learning|algorithms|early detection of cancer
• ISSN: 2096-529X
• DOI: 10.13362/j.jpmed.202540079

Objective To establish an early screening model for cervical cancer based on single-cell Raman spectroscopy (SCRS) combined with machine learning algorithms, and to assess the performance of the model. Methods Cervical exfoliated cell samples were collected from 128 patients who were treated in our hospital from September 2023 to June 2024, among whom 65 had normal results of ThinPrep cytologic test (TCT), 35 had abnormal TCT results, and 28 did not receive TCT. R language was used to divide the 100 cervical exfoliated cell samples with TCT results into training set and test set at a ratio of 8∶2, and SCRS was performed for all samples. Based on the SCRS data of the training set, 7 machine learning algorithms (KNN, PLS, LDA, RF, SVM, SVMRBF, and Stack) were used to establish an early screening model for cervical cancer, which was applied in the test set to identify the optimal model. The optimal model was then used to predict the TCT results of 100 cervical exfoliated cell samples in the training and test set