SynthesisAsian Pacific journal of cancer prevention : APJCP2025
Identification of Factors Affecting Prostate Cancer Using Machine Learning Methods: A Systematic Review.
Synthesis in Asian Pacific journal of cancer prevention : APJCP, 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.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundProstate cancer is identified as the second cause of malignancy worldwide and the fifth cause of death among men. Considering the upward trend in cancer incidence and mortality rate due to this disease, the identification of risk factors can be of great help in prevention and conservative measures. Also, due to the significant growth in artificial intelligence and machine learning methods, many risk factors can be studied by identifying the most commonly used methods.
methodsThe articles reviewed in this study were from 4 main databases: PubMed, Scopus, Web of Science, and IEEE Xplore. This systematic review was based on Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Searching the databases was conducted from the beginning of 2015 to February 17, 2024 were included. Only the articles investigating factors affecting prostate cancer using machine learning are included in this systematic review. Non-English language studies, studies that did not involve human participants, review studies, meta-analyses, letters to editors, and commentary were excluded.
resultsThe findings showed that China had the most research in identifying prostate cancer risk factors with machine learning algorithms. Age, PSA level (prostate-specific antigen), tPSA (total PSA), fPSA (free PSA), and PSAD (PSA density) were identified as the most important risk factors in prostate cancer. R-software and Python were most employed in the data analysis. Random forest, support vector machine, and logistic regression were utilized more than other machine learning methods. Among data sources, MCC-Spain, SEER (surveillance, Epidemiology, and End Results), PLCO (National Cancer Institute's Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial), and NCBI (National Center for Biotechnology Information) were registries that were used in the studies.
conclusionThis research can help researchers use machine learning methods with better performance and registered data sources and identify the most influential risk factors for prostate cancer prevention and screening.
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.