{"title":"围手术期癌症患者新辅助免疫治疗的免疫相关不良事件:一项机器学习驱动的长达十年的信息学研究。","authors":"Song-Bin Guo, Deng-Yao Liu, Rong Hu, Zhen-Zhong Zhou, Yuan Meng, Hai-Long Li, Wei-Juan Huang, Xiao-Peng Tian","doi":"10.1136/jitc-2024-011040","DOIUrl":null,"url":null,"abstract":"<p><p>Research on neoadjuvant immunotherapy (NAI) is increasingly focusing on immunotherapy-related adverse events (AEs). However, many unknowns remain in this field. Hence, through the machine learning (ML)-driven informatics analysis, this study aimed to profile the global decade-long scientific landscape of AEs of NAI and further reveal its critical issues and directions that deserve deeper exploration. During the past decade, the amount of research in the field of NAI safety has displayed a positive trend (annual growth rate: 30.2%), and it has achieved good global collaboration (international coauthorship: 17.43%). Using an unsupervised clustering algorithm, we identified six dominant research clusters, among which Cluster 1 (standardizing response assessment criteria for NAI to minimize its adverse reactions; average citation=34.86±95.48) had the highest impact and Cluster 6 (efficacy and safety of multiple therapy patterns combination) was an emerging research cluster (temporal central tendency=2022.43, research effort dispersion=0.52), with \"irAEs\" (s=0.4242 (95% CI: 0.01142 to 0.8371), R<sup>2</sup>=0.4125, p=0.0453), \"ICIs\" (immune checkpoint inhibitors) (s=1.127 (95% CI: 0.5403 to 1.714), R<sup>2</sup>=0.7103, p=0.0022), and \"efficacy and safety\" (s=0.5455 (95% CI: 0.1145 to 0.9764), R<sup>2</sup>=0.5157, p=0.0193) showing significant overall growth. More importantly, further hotspot burst analysis indicated \"ICI\" and \"efficacy and safety\" as the emerging research focuses, demonstrating that scholars in the field are increasingly aware of the importance of balancing NAI efficacy and safety. In conclusion, this study presents ML-derived evidence that outlines the safety challenges of NAI and highlights the importance of balancing its efficacy and safety for its application in patients with perioperative cancer.</p>","PeriodicalId":14820,"journal":{"name":"Journal for Immunotherapy of Cancer","volume":"13 8","pages":""},"PeriodicalIF":10.6000,"publicationDate":"2025-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12374627/pdf/","citationCount":"0","resultStr":"{\"title\":\"Immune-related adverse events of neoadjuvant immunotherapy in patients with perioperative cancer: a machine-learning-driven, decade-long informatics investigation.\",\"authors\":\"Song-Bin Guo, Deng-Yao Liu, Rong Hu, Zhen-Zhong Zhou, Yuan Meng, Hai-Long Li, Wei-Juan Huang, Xiao-Peng Tian\",\"doi\":\"10.1136/jitc-2024-011040\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>Research on neoadjuvant immunotherapy (NAI) is increasingly focusing on immunotherapy-related adverse events (AEs). However, many unknowns remain in this field. Hence, through the machine learning (ML)-driven informatics analysis, this study aimed to profile the global decade-long scientific landscape of AEs of NAI and further reveal its critical issues and directions that deserve deeper exploration. During the past decade, the amount of research in the field of NAI safety has displayed a positive trend (annual growth rate: 30.2%), and it has achieved good global collaboration (international coauthorship: 17.43%). Using an unsupervised clustering algorithm, we identified six dominant research clusters, among which Cluster 1 (standardizing response assessment criteria for NAI to minimize its adverse reactions; average citation=34.86±95.48) had the highest impact and Cluster 6 (efficacy and safety of multiple therapy patterns combination) was an emerging research cluster (temporal central tendency=2022.43, research effort dispersion=0.52), with \\\"irAEs\\\" (s=0.4242 (95% CI: 0.01142 to 0.8371), R<sup>2</sup>=0.4125, p=0.0453), \\\"ICIs\\\" (immune checkpoint inhibitors) (s=1.127 (95% CI: 0.5403 to 1.714), R<sup>2</sup>=0.7103, p=0.0022), and \\\"efficacy and safety\\\" (s=0.5455 (95% CI: 0.1145 to 0.9764), R<sup>2</sup>=0.5157, p=0.0193) showing significant overall growth. More importantly, further hotspot burst analysis indicated \\\"ICI\\\" and \\\"efficacy and safety\\\" as the emerging research focuses, demonstrating that scholars in the field are increasingly aware of the importance of balancing NAI efficacy and safety. In conclusion, this study presents ML-derived evidence that outlines the safety challenges of NAI and highlights the importance of balancing its efficacy and safety for its application in patients with perioperative cancer.</p>\",\"PeriodicalId\":14820,\"journal\":{\"name\":\"Journal for Immunotherapy of Cancer\",\"volume\":\"13 8\",\"pages\":\"\"},\"PeriodicalIF\":10.6000,\"publicationDate\":\"2025-08-21\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12374627/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal for Immunotherapy of Cancer\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.1136/jitc-2024-011040\",\"RegionNum\":1,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"IMMUNOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal for Immunotherapy of Cancer","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1136/jitc-2024-011040","RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"IMMUNOLOGY","Score":null,"Total":0}
Immune-related adverse events of neoadjuvant immunotherapy in patients with perioperative cancer: a machine-learning-driven, decade-long informatics investigation.
Research on neoadjuvant immunotherapy (NAI) is increasingly focusing on immunotherapy-related adverse events (AEs). However, many unknowns remain in this field. Hence, through the machine learning (ML)-driven informatics analysis, this study aimed to profile the global decade-long scientific landscape of AEs of NAI and further reveal its critical issues and directions that deserve deeper exploration. During the past decade, the amount of research in the field of NAI safety has displayed a positive trend (annual growth rate: 30.2%), and it has achieved good global collaboration (international coauthorship: 17.43%). Using an unsupervised clustering algorithm, we identified six dominant research clusters, among which Cluster 1 (standardizing response assessment criteria for NAI to minimize its adverse reactions; average citation=34.86±95.48) had the highest impact and Cluster 6 (efficacy and safety of multiple therapy patterns combination) was an emerging research cluster (temporal central tendency=2022.43, research effort dispersion=0.52), with "irAEs" (s=0.4242 (95% CI: 0.01142 to 0.8371), R2=0.4125, p=0.0453), "ICIs" (immune checkpoint inhibitors) (s=1.127 (95% CI: 0.5403 to 1.714), R2=0.7103, p=0.0022), and "efficacy and safety" (s=0.5455 (95% CI: 0.1145 to 0.9764), R2=0.5157, p=0.0193) showing significant overall growth. More importantly, further hotspot burst analysis indicated "ICI" and "efficacy and safety" as the emerging research focuses, demonstrating that scholars in the field are increasingly aware of the importance of balancing NAI efficacy and safety. In conclusion, this study presents ML-derived evidence that outlines the safety challenges of NAI and highlights the importance of balancing its efficacy and safety for its application in patients with perioperative cancer.
期刊介绍:
The Journal for ImmunoTherapy of Cancer (JITC) is a peer-reviewed publication that promotes scientific exchange and deepens knowledge in the constantly evolving fields of tumor immunology and cancer immunotherapy. With an open access format, JITC encourages widespread access to its findings. The journal covers a wide range of topics, spanning from basic science to translational and clinical research. Key areas of interest include tumor-host interactions, the intricate tumor microenvironment, animal models, the identification of predictive and prognostic immune biomarkers, groundbreaking pharmaceutical and cellular therapies, innovative vaccines, combination immune-based treatments, and the study of immune-related toxicity.