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胰腺癌预后预测模型的全球研究趋势与热点:一项文献计量分析

Global research trends and hotspots in prognostic prediction models for pancreatic cancer: a bibliometric analysis.

作者信息

Ouyang Siyuan, Zhang Jing, Liu Fuyao, Jiang Qi, Xing Wei, Chen Jie, Zhang Jinggang

机构信息

Department of Radiology, The Third Affiliated Hospital of Soochow University, Changzhou, China.

Magnetic Resonance Research Collaboration Team, Siemens Healthineers Ltd., Shanghai, China.

出版信息

Front Oncol. 2025 Jul 10;15:1588735. doi: 10.3389/fonc.2025.1588735. eCollection 2025.


DOI:10.3389/fonc.2025.1588735
PMID:40708943
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC12286805/
Abstract

BACKGROUND: Pancreatic cancer is a highly aggressive malignancy of the digestive system, characterized by insidious onset and rapid progression. Most cases are diagnosed at advanced stages, complicating surgical resection and presenting significant challenges for clinical treatment. Recent advancements have emphasized individualized treatment strategies tailored to patients' specific conditions. Consequently, accurate preoperative assessment is crucial, highlighting the urgent need to develop more reliable predictive models to guide personalized treatment plans. METHODS: A systematic literature search was conducted using Web of Science Core Collection (WoSCC) database, covering publications from January 1, 1995, to October 25, 2024. A comprehensive bibliometric analysis was performed employing analytical tools such as VOSviewer, CiteSpace and Microsoft Excel. RESULTS: This study includes 919 publications authored by 6716 researchers from 3727 institutions in 222 countries and regions. The articles were published in 301 journals, with 1,640 distinct keywords and 25,910 references. China led in publication volume, while the United States garnered the most citations. The top three research institutions in this field were Fudan University, Shanghai Jiao Tong University, and Sun Yat-sen University. Yu Xianjun from Fudan University emerged as the most prolific author with the highest citation count. had the highest publication volume, while the received the most citations. Medical imaging, biochemistry, immunology, bioinformatics, genetics, and interdisciplinary integrative research are the main research disciplines in the field of prognosis prediction for pancreatic cancer. The results of keyword co-occurrence and literature co-citation analysis revealed emerging hotspots and trends in this field, including CA19-9, CT, inflammation, machine learning, tumor microenvironment, radiomics, genes, nomograms, randomized controlled trials, long-term survival, and metastasis. CONCLUSION: This bibliometric analysis provides an overview of research conducted over the past three decades, offering insights into the current state of knowledge and outlining directions for future studies on prognosis prediction models for pancreatic cancer. Biochemical indicators have consistently emerged as key research focal points. The tumor microenvironment represents a currently popular research direction, while bioinformatics, medical imaging, and artificial intelligence are gaining traction as future trends in this field. In the future, prognostic models for pancreatic cancer require further refinement to ensure reliable guidance for therapeutic decision-making.

摘要

背景:胰腺癌是消化系统一种侵袭性很强的恶性肿瘤,其特点是起病隐匿、进展迅速。大多数病例在晚期才被诊断出来,这使得手术切除变得复杂,给临床治疗带来了重大挑战。最近的进展强调了根据患者具体情况制定个性化的治疗策略。因此,准确的术前评估至关重要,这突出表明迫切需要开发更可靠的预测模型来指导个性化治疗方案。 方法:使用科学网核心合集(WoSCC)数据库进行系统的文献检索,涵盖1995年1月1日至2024年10月25日发表的文献。采用VOSviewer、CiteSpace和Microsoft Excel等分析工具进行全面的文献计量分析。 结果:本研究纳入了来自222个国家和地区3727个机构的6716名研究人员撰写的919篇文献。这些文章发表在301种期刊上,有1640个不同的关键词和25910条参考文献。中国在发文量上领先,而美国获得的引用次数最多。该领域排名前三的研究机构是复旦大学、上海交通大学和中山大学。复旦大学的虞先军是发文量最多且被引次数最高的多产作者。 发文量最高,而 获得的引用次数最多。医学影像学、生物化学、免疫学、生物信息学、遗传学以及跨学科综合研究是胰腺癌预后预测领域的主要研究学科。关键词共现和文献共被引分析结果揭示了该领域新出现的热点和趋势,包括CA19-9、CT、炎症、机器学习、肿瘤微环境、放射组学、基因、列线图、随机对照试验、长期生存和转移。 结论:这项文献计量分析概述了过去三十年的研究情况,深入了解了当前的知识状态,并为未来胰腺癌预后预测模型的研究指明了方向。生化指标一直是关键的研究重点。肿瘤微环境是当前热门的研究方向,而生物信息学、医学影像学和人工智能作为该领域未来的趋势正在获得关注。未来,胰腺癌的预后模型需要进一步完善,以确保为治疗决策提供可靠的指导。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c878/12286805/fdae1b95d2d0/fonc-15-1588735-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c878/12286805/a186f91c57d4/fonc-15-1588735-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c878/12286805/fca2597f181e/fonc-15-1588735-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c878/12286805/fdae1b95d2d0/fonc-15-1588735-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c878/12286805/a186f91c57d4/fonc-15-1588735-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c878/12286805/fca2597f181e/fonc-15-1588735-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c878/12286805/fdae1b95d2d0/fonc-15-1588735-g003.jpg

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本文引用的文献

[1]
Development and validation of a CT-based radiomics model to predict survival-graded fibrosis in pancreatic ductal adenocarcinoma.

Int J Surg. 2025-1-1

[2]
Cancer incidence and mortality in China, 2022.

J Natl Cancer Cent. 2024-2-2

[3]
Challenges and limitations in applying radiomics to PET imaging: Possible opportunities and avenues for research.

Comput Biol Med. 2024-9

[4]
Histology-Based Radiomics for [F]FDG PET Identifies Tissue Heterogeneity in Pancreatic Cancer.

J Nucl Med. 2024-7-1

[5]
Trends and hotspots of prognostic factors for pancreatic ductal adenocarcinoma from 2013 to 2022.

Hepatobiliary Surg Nutr. 2024-4-3

[6]
Cancer statistics, 2024.

CA Cancer J Clin. 2024

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N Engl J Med. 2023-3-30

[8]
Development and validation of an MRI-radiomics nomogram for the prognosis of pancreatic ductal adenocarcinoma.

Front Oncol. 2023-2-24

[9]
Immediate surgery compared with short-course neoadjuvant gemcitabine plus capecitabine, FOLFIRINOX, or chemoradiotherapy in patients with borderline resectable pancreatic cancer (ESPAC5): a four-arm, multicentre, randomised, phase 2 trial.

Lancet Gastroenterol Hepatol. 2023-2

[10]
Peritumoral and Intratumoral Texture Features Based on Multiparametric MRI and Multiple Machine Learning Methods to Preoperatively Evaluate the Pathological Outcomes of Pancreatic Cancer.

J Magn Reson Imaging. 2023-8

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