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。

IF 10.6 1区 医学 Q1 IMMUNOLOGY
Song-Bin Guo, Deng-Yao Liu, Rong Hu, Zhen-Zhong Zhou, Yuan Meng, Hai-Long Li, Wei-Juan Huang, Xiao-Peng Tian
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引用次数: 0

摘要

新辅助免疫治疗(NAI)的研究越来越关注免疫治疗相关不良事件(ae)。然而,这一领域仍有许多未知之处。因此,本研究旨在通过机器学习(ML)驱动的信息学分析,概述全球近十年来NAI ae的科学格局,并进一步揭示其值得深入探索的关键问题和方向。近十年来,NAI安全领域的研究数量呈现出积极的趋势(年增长率为30.2%),并取得了良好的全球合作(国际合作:17.43%)。使用无监督聚类算法,我们确定了6个优势研究聚类,其中聚类1(标准化NAI的反应评估标准,以最大限度地减少其不良反应;平均引用数=34.86±95.48)的影响最大,第6类(多种治疗模式联合的疗效和安全性)是一个新兴的研究类(时间集中趋势=2022.43,研究努力离散度=0.52),其中“irae”(s=0.4242 (95% CI: 0.01142 ~ 0.8371), R2=0.4125, p=0.0453),“ICIs”(免疫检查点抑制剂)(s=1.127 (95% CI: 0.5403 ~ 1.714), R2=0.7103, p=0.0022),“疗效和安全性”(s=0.5455 (95% CI: 0.5455):0.1145 ~ 0.9764), R2=0.5157, p=0.0193),总体增长显著。更重要的是,进一步的热点突发分析表明,“ICI”和“疗效与安全性”是新兴的研究热点,表明该领域的学者越来越意识到平衡NAI疗效与安全性的重要性。总之,本研究提出了ml衍生的证据,概述了NAI的安全性挑战,并强调了在围手术期癌症患者中应用NAI时平衡其有效性和安全性的重要性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Immune-related adverse events of neoadjuvant immunotherapy in patients with perioperative cancer: a machine-learning-driven, decade-long informatics investigation.

Immune-related adverse events of neoadjuvant immunotherapy in patients with perioperative cancer: a machine-learning-driven, decade-long informatics investigation.

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.

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来源期刊
Journal for Immunotherapy of Cancer
Journal for Immunotherapy of Cancer Biochemistry, Genetics and Molecular Biology-Molecular Medicine
CiteScore
17.70
自引率
4.60%
发文量
522
审稿时长
18 weeks
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.
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