ArticleAsian journal of andrology
Prostate cancer prediction using the random forest algorithm that takes into account transrectal ultrasound findings, age, and serum levels of prostate-specific antigen.
Article in Asian journal of andrology. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers, 2 of them syntheses that pooled it.
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Who cites it
24 citing papers in PubMed, 2 syntheses or guidelines pooled it, 59 citations in OpenAlex.
- Identification of Factors Affecting Prostate Cancer Using Machine Learning Methods: A Systematic Review.Asian Pacific journal of cancer prevention : APJCP · 2025Pooled it
- Radiomic and Genomic Machine Learning Method Performance for Prostate Cancer Diagnosis: Systematic Literature Review.Journal of medical Internet research · 2021Pooled it
- Integrating multimodal clinical data with a large model for prostate cancer diagnosis.NPJ digital medicine · 2026Article
- Machine learning algorithms for identifying tuberculous pericardial effusion: insights from a retrospective study in Vietnam.BMC medical informatics and decision making · 2026Article
- Comparative Performance of Machine Learning Models in Reducing Unnecessary Targeted Prostate Biopsies.European urology oncology · 2026Article
- Artificial Intelligence in Cancer Diagnosis: A Game-changer in Healthcare.Current pharmaceutical biotechnology · 2025Review
- Artificial intelligence in prostate cancer: navigating the new frontier of precision uro-oncology.American journal of clinical and experimental urology · 2025Review
- Machine learning in image-based outcome prediction after radiotherapy: A review.Journal of applied clinical medical physics · 2025Review
- Article
- Diagnosis of benign and malignant peripheral lung lesions based on a feature model constructed by the random forest algorithm for grayscale and contrast-enhanced ultrasound.Frontiers in oncology · 2024Article
- Machine learning-based prediction model and visual interpretation for prostate cancer.BMC urology · 2023Article
- Review
- Prediction of Atrial Fibrillation in Hospitalized Elderly Patients With Coronary Heart Disease and Type 2 Diabetes Mellitus Using Machine Learning: A Multicenter Retrospective Study.Frontiers in public health · 2022Article
- Identifying discriminative features for diagnosis of Kashin-Beck disease among adolescents.BMC musculoskeletal disorders · 2021Article
- Long ncRNA MALAT1 promotes cell proliferation, migration, and invasion in prostate cancer via sponging miR-145.Translational andrology and urology · 2021Article
- Article
- Article
- Identifying tuberculous pleural effusion using artificial intelligence machine learning algorithms.Respiratory research · 2019Article
- Machine learning methods can more efficiently predict prostate cancer compared with prostate-specific antigen density and prostate-specific antigen velocity.Prostate international · 2019Article
- Factors Associated with HIV Testing Among Participants from Substance Use Disorder Treatment Programs in the US: A Machine Learning Approach.AIDS and behavior · 2017Article
Corrections and comments
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Authors and funding
7 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The aim of this study is to evaluate the ability of the random forest algorithm that combines data on transrectal ultrasound findings, age, and serum levels of prostate-specific antigen to predict prostate carcinoma. Clinico-demographic data were analyzed for 941 patients with prostate diseases treated at our hospital, including age, serum prostate-specific antigen levels, transrectal ultrasound findings, and pathology diagnosis based on ultrasound-guided needle biopsy of the prostate. These data were compared between patients with and without prostate cancer using the Chi-square test, and then entered into the random forest model to predict diagnosis. Patients with and without prostate cancer differed significantly in age and serum prostate-specific antigen levels (P < 0.001), as well as in all transrectal ultrasound characteristics (P < 0.05) except uneven echo (P = 0.609). The random forest model based on age, prostate-specific antigen and ultrasound predicted prostate cancer with an accuracy of 83.10%, sensitivity of 65.64%, and specificity of 93.83%. Positive predictive value was 86.72%, and negative predictive value was 81.64%. By integrating age, prostate-specific antigen levels and transrectal ultrasound findings, the random forest algorithm shows better diagnostic performance for prostate cancer than either diagnostic indicator on its own. This algorithm may help improve diagnosis of the disease by identifying patients at high risk for biopsy.
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