From Text to Insight: A Natural Language Processing-Based Analysis of Burst and Research Trends in HER2-low Breast Cancer Patients

乳腺癌 自然语言处理 计算机科学 癌症 数据科学 人工智能 心理学 语言学 医学 内科学 哲学
作者
Muyao Li,Ang Zheng,Mingjie Song,Feng Jin,Mengyang Pang,Yuchong Zhang,Ying Wu,Xin Li,Mingfang Zhao,Zhi Li
出处
期刊:Ageing Research Reviews [Elsevier BV]
卷期号:106: 102692-102692
标识
DOI:10.1016/j.arr.2025.102692
摘要

With the intensification of population aging, the proportion of elderly breast cancer patients is continuously increasing, among which those with low HER2 expression account for approximately 45 %-55 % of all cases within traditional HER2-negative breast cancer. Concurrently, the significant therapeutic effect of T-DXd on patients with HER2-low tumors has brought this group into the public spotlight. Since the clinical approval of T-DXd in 2019, there has been a significant vertical surge in the volume of publications within this domain. We analyzed 512 articles on HER2-low breast cancer from the Web of Science Core Collection using bibliometrics, topic modeling, and knowledge graph techniques to summarize the current state and trends of research in this domain. Research efforts are particularly concentrated in the United States and China. Our analysis revealed six main research directions: HER2 detection, omics and clinical biomarkers, basic and translational research, neoadjuvant therapy and prognosis, progress of ADC drugs and clinical trials. To enhance the therapeutic efficacy and safety of antibodydrug conjugates (ADCs), researchers are actively exploring potential drug candidates other than T-DXd, with numerous ADC drugs emerging in clinical practice and trials. By incorporating emerging treatment strategies such as immunotherapy and employing circulating tumor cell (CTC) detection techniques, progress has been made toward improving the prognosis of patients with low HER2 expression. We believe that these research efforts hold promise as compelling evidence that HER2-low breast cancer may constitute a distinct and independent subtype.
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