Bibliometric analysis of the global scientific production on machine learning applied to different cancer types-Reference-Cited by-同舟云学术

Bibliometric analysis of the global scientific production on machine learning applied to different cancer types

Published:2023-02-20 Issue: Volume: Page:
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Author:

Ruiz-Fresneda Miguel Angel¹^ORCID,Gijón Alfonso²,Morales-Álvarez Pablo²

Affiliation:

1. University of Granada

2. University of Granada: Universidad de Granada

Abstract

Abstract Cancer disease is one of the main causes of death in the world, with million annual cases in the last decades. The need to find a cure has stimulated the search for efficient treatments and diagnostic procedures. One of the most promising tools that has emerged against cancer in recent years is Machine Learning (ML), which has raised a huge number of scientific papers published in a relatively short period of time. The present study analyzes global scientific production on ML applied to the most relevant cancer types through various bibliometric indicators. We find that over 30000 studies have been published so far and observe that cancers with the highest number of published studies using ML (breast, lung, and colon cancer) are those with the highest incidence, being USA and China the main scientific producers on the subject. Interestingly, the role of China and Japan in stomach cancer is correlated with the number of cases of this cancer type in Asia (78% of the worldwide cases). Knowing the countries and institutions that most study each area can be of great help for improving international collaborations between research groups and countries. Our analysis shows that medical and computer science journals lead the number of publications on the subject and could be useful for researchers in the field. Finally, keyword co-occurrence analysis suggest that ML-cancer research trends are focused not only on the use of ML as an effective diagnostic method, but also for the improvement of radiotherapy- and chemotherapy-based treatments.

Publisher

Research Square Platform LLC

Reference61 articles.

1. Kangtao Wang and Chenzhe Feng and Ming Li and Qian Pei and Yuqiang Li and Hong Zhu and Xiangping Song and Haiping Pei and Fengbo Tan (2020) A bibliometric analysis of 23,492 publications on rectal cancer by machine learning: basic medical research is needed. Therapeutic Advances in Gastroenterology 13 https://doi.org/10.1177/1756284820934594, 17562848, Background and Aims: The aim of this study was to analyse the landscape of publications on rectal cancer

2. (RC) over the past 25 years by machine learning and semantic analysis. Methods: Publications indexed in PubMed under the Medical Subject Headings (MeSH) term 'Rectal Neoplasms ' from 1994 to 2018 were downloaded in September 2019. R and Python were used to extract publication date, MeSH terms and abstract from the metadata of each publication for bibliometric assessment. Latent Dirichlet allocation was applied to analyse the text from the articles ' abstracts to identify more specific research topics. Louvain algorithm was used to establish a topic network resulting in identifying the relationship between the topics. Results: A total of 23,492 papers published were identified and analysed in this study. The changes of research focus were analysed by the changing of MeSH terms. Studied contents extracted from the publications were divided into five areas, including surgical intervention, radiotherapy and chemotherapy intervention, clinical case management, epidemiology and cancer risk as well as prognosis studies. Conclusions: The number of publications indexed on RC has expanded rapidly over the past 25 years. Studies on RC have mainly focused on five areas. However, studies on basic research, postoperative quality of life and cost-effective research were relatively lacking. It is predicted that basic research, inflammation and some other research fields might become the potential hotspots in the future.

3. Nicole L. Stout and Catherine M. Alfano and Christopher W. Belter and Ralph Nitkin and Alison Cernich and Karen Lohmann Siegel and Leighton Chan (2018) A bibliometric analysis of the landscape of cancer rehabilitation research (1992-2016). Journal of the National Cancer Institute 110 https://doi.org/10.1093/jnci/djy108, 8, 14602105, Cancer rehabilitation research has accelerated as great attention has focused on improving survivorship care. Recent expert consensus has attempted to prioritize research needs and suggests greater focus on studying physical functioning of survivors. However, no analysis of the publication landscape has substantiated these proposed needs. This manuscript provides an analysis of PubMed indexed articles related to cancer rehabilitation published between 1992 and 2017. A total of 22 171 publications were analyzed using machine learning and text analysis to assess publication metrics, topic areas of emphasis, and their interrelationships through topic similarity networks. Publications have increased at a rate of 136 articles per year. Approximately 10% of publications were funded by the National Institutes of Health institutes and centers, with the National Cancer Institute being the most prominent funder. The greatest volume and rate of publication increase were in the topics of Cognitive and Behavioral Therapies and Psychological Interventions, followed by Depression and Exercise Therapy. Four research topic similarity networks were identified and provide insight on areas of robust publication and notable deficits. Findings suggest that publication emphasis has strongly supported cognitive, behavioral, and psychological therapies; however, studies of functional morbidity and physical rehabilitation research are lacking. Three areas of publication deficits are noted: research on populations outside of breast, prostate, and lung cancers; methods for integrating physical rehabilitation services with cancer care, specifically regarding functional screening and assessment; and physical rehabilitation interventions. These deficits align with the needs identified by expert consensus and support the supposition that future research should emphasize a focus on physical rehabilitation.

4. Zakia Salod and Yashik Singh (2020) A five-year (2015 to 2019) analysis of studies focused on breast cancer prediction using machine learning: A systematic review and bibliometric analysis. Journal of Public Health Research 9 https://doi.org/10.4081/jphr.2020.1772, 1, 22799036, The objective 1 of this study was to investigate trends in breast cancer (BC) prediction using machine learning (ML) publications by analysing country, first author, journal, institutional collaborations and co-occurrence of author keywords. The objective 2 was to provide a review of studies on BC prediction using ML and a blood analysis dataset (Breast Cancer Coimbra Dataset [BCCD]), and the objective 3 was to provide a brief review of studies based on BC prediction using ML and patients ’ fine needle aspirate cytology data (Wisconsin Breast Cancer Dataset [WBCD]). The design of this study was as follows: for objective 1: bibliometric analysis, data source PubMed (2015-2019); for objective 2: systematic review, data source: Google and Google Scholar (20182019); for objective 3: systematic review, data source: Google Scholar (2016-2019). The inclusion criteria for objective 1 were all publication results yielded from the searches. All English papers that had a ‘PDF ’ option from the search results were included for objective 2. A sample of the ‘PDF ’ English papers were included for objective 3. All 116 female patients from the BCCD, consisting of 64 positive BC patients and 52 controls were included in the study for objective 2. For the WBCD, all 699 female patients comprising of 458 with a benign BC tumour and 241 with a malignant BC tumour were included for objective 3. All 2928 publications were included for objective 1. The results showed that the United States of America (USA) produced the highest number of publications (n=803). In total, 2419 first authors contributed towards the publications. Breast Cancer Research and Treatment was the highest ranked journal. Institutional collaborations mainly occurred within the USA. The use of ML for BC screening and detection was the most researched topic. A total of 19 distinct papers were included for objectives 2 and 3. The findings from these studies were never presented to clinicians for validations. In conclusion, the use of ML for BC screening and detection is promising..

5. Shubhangi A. Joshi and Anupkumar M. Bongale and Arunkumar Bongale (2021) Breast Cancer Detection from Histopathology Images using Machine Learning Techniques: A Bibliometric Analysis. Library Philosophy and Practice 202115220222, Computer aided diagnosis has become upcoming area of research over past few years. With the advent of machine learning and especially deep learning techniques, the scenario of work ow management in healthcare sector is changing drastically. Artificial intelligence has shown potential in the field of breast cancer care. With datasets for machine learning frameworks getting eventually richer with time, we can definitely get newer insights in the field of breast cancer care. This will help in narrowing down the treatment range for patients and increasing patient survivability. The purpose of this study was to perform bibliometric analysis of the literature in the area of breast cancer detection using machine learning. Analysis was done for various elements like publication types, highly in uential authors, most prominent journals, institutional affiliations, main keywords, etc. This analysis may direct future researchers by giving thorough quantitative evaluation of research documents in the field of breast cancer detection using machine learning.