ArticleCancer biology & medicine2023
A machine learning model to predict efficacy of neoadjuvant therapy in breast cancer based on dynamic changes in systemic immunity.
Article in Cancer biology & medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Integrating MRI radiomics and deep learning for predicting pathologic complete response to neoadjuvant chemotherapy in breast cancer: a multicenter study.BMC medical imaging · 2026Article
- Value of Endoscopic Ultrasonography for Distinguishing Malignant from Benign Non-pancreatic Periampullary Lesions: An Explainable Machine Learning Study.Digestive diseases and sciences · 2026Article
- [Artificial intelligence in predicting pathological complete response to neoadjuvant chemotherapy for breast cancer: current advances and challenges].Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi · 2025Review
- Clinical performance of a machine learning-based model for detecting lymph node metastasis in papillary thyroid carcinoma: A multicenter study.International journal of surgery (London, England) · 2025Article
- Prediction of neoadjuvant chemotherapy efficacy and prognostic biomarker analysis in patients with triple-negative breast cancer.Frontiers in pharmacology · 2025Article
- Lysine lactylation (Kla) might be a novel therapeutic target for breast cancer.BMC medical genomics · 2023Article
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Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveNeoadjuvant therapy (NAT) has been widely implemented as an essential treatment to improve therapeutic efficacy in patients with locally-advanced cancer to reduce tumor burden and prolong survival, particularly for human epidermal growth receptor 2-positive and triple-negative breast cancer. The role of peripheral immune components in predicting therapeutic responses has received limited attention. Herein we determined the relationship between dynamic changes in peripheral immune indices and therapeutic responses during NAT administration.
methodsPeripheral immune index data were collected from 134 patients before and after NAT. Logistic regression and machine learning algorithms were applied to the feature selection and model construction processes, respectively.
resultsPeripheral immune status with a greater number of CD3
conclusionsStatistically significant relationships between several specific immune indices and the efficacy of NAT were revealed. A random forest model based on dynamic changes in peripheral immune indices showed robust performance in predicting NAT efficacy.
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