ArticleWorld journal of gastrointestinal oncology2025
Research status and trends of deep learning in colorectal cancer (2011-2023): Bibliometric analysis and visualization.
Article in World journal of gastrointestinal oncology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed.
- Deep multimodal state-space fusion of endoscopic-radiomic and clinical data for survival prediction in colorectal cancer.NPJ digital medicine · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundColorectal cancer (CRC) is the third-most prevalent cancer and the cancer with the second-highest mortality rate worldwide, representing a high public health burden. Deep learning (DL) offers advantages in the diagnosis, identification, localization, classification and prognosis of CRC patients. However, few bibliometric analyses of research hotspots and trends in the field have been performed.
aimTo use bibliometric approaches to analyze and visualize the current research state and development trend of DL in CRC as well as to anticipate future research directions and hotspots.
methodsDatasets were retrieved from the Web of Science Core Collection for the period January 2011 to December 2023. Scimago Graphica (1.0.45), VOSviewer (1.6.20) and CiteSpace (6.3.1) were used to analyze and visualize the nation, institution, journal, author, reference and keyword indicators. Origin (2022) was utilized for plotting, and Excel (2021) was used to construct the tables.
resultsA total of 1275 publications in 538 journals from 74 countries and 2267 institutions were collected. The number of annual publications has increased over time. China (371, 29.1%), the United States (265, 20.8%) and Japan (155, 12.2%) contributed significantly to the number of articles published, accounting for 62.1% of the total publications. The United States had the strongest ties to other nations. Sun Yat-sen University in China had the highest number of publications (32). The journal with the most publications was
conclusionThis study highlights the current status and most active directions of the use of DL in CRC. This approach has important applications in the identification, diagnosis, localization, classification and prognosis of the disease and will remain a central focus in the future.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.