SYSTEMATIC REVIEW article
Front. Oncol.
Sec. Cancer Epidemiology and Prevention
Volume 15 - 2025 | doi: 10.3389/fonc.2025.1556521
Progress and Current Trends in Prediction Models for the Occurrence and Prognosis of Cancer and Cancer-Related Complications: A Bibliometric and Visualization Analysis
Provisionally accepted
- Department of Pharmacy, Chongqing University Cancer Hospital, Chongqing, China
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Objective: Prediction models, estimating disease/outcome probabilities, are widely used in cancer research. This study aims to identify hotspots and future directions in cancer-related prediction modeling using bibliometrics.Methods: A comprehensive literature search was conducted in the Science Citation Index Expanded (SCIE) from the Web of Science Core Collection (WoSCC) up to November 15, 2024, focusing on cancer-related prediction modeling research. Co-occurrence analyses of countries, institutions, authors, journals, and keywords were conducted using VOSviewer 1.6.20. Additionally, keyword clustering, timeline visualization, and burst term analysis were performed with CiteSpace 6.3.Results: A total of 1,661 records were retrieved from the SCIE. After deduplication and eligibility screening, 1,556 publications were included in the analysis. The bibliometric analysis revealed a consistent annual increase in cancer-related prediction model research, with China and the United States emerging as the leading contributors. The United States, England, and the Netherlands exhibited the strongest collaborative networks. The most frequent keywords, excluding "prediction model" and "predictive model", included nomogram (frequency=192), survival (191), risk (121), prognosis (112), breast cancer (103), carcinoma (93), validation (87), surgery (85), diagnosis (83), chemotherapy (80), and machine learning (77). Besides, the timeline view analysis indicated that the "#7 machine learning" cluster is experiencing vigorous growth.Cancer-related prediction models are rapidly advancing, especially in prognostic models.Emerging modeling techniques, such as neural networks and deep learning algorithms, are likely to play a pivotal role in current and future cancer-related prediction model research. Systematic reviews 4 of cancer-related predictive models, guiding clinicians in selecting the optimal model for a given health concern, may emerge as potential research directions in this field.
Keywords: Cancer Prediction Models: Trends & Bibliometric Analysis This study does not involve human participants SL: Conceptualization, Methodology, Data curation, Visualization, Formal analysis and interpretation of data, and Writing-original draft, WL: Conceptualization, Methodology, Supervision, Writing-review and editing, XW: Conceptualization, Writing-review and editing, and Funding acquisition, WCh: Conceptualization, Writing-review and editing, and Funding acquisition cancer, Prediction models, Bibliometrics, Hotspots and trends
Received: 07 Jan 2025; Accepted: 10 Jun 2025.
Copyright: © 2025 Li, Li, Wang and Chen. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence:
Xiaoxiao Wang, Department of Pharmacy, Chongqing University Cancer Hospital, Chongqing, China
Wanyi Chen, Department of Pharmacy, Chongqing University Cancer Hospital, Chongqing, China
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