ArticleScientific reports2023
Machine-learning predicts time-series prognosis factors in metastatic prostate cancer patients treated with androgen deprivation therapy.
Article in Scientific reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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Who cites it
19 citing papers in PubMed.
- Predicting disease progression and mortality in prostate cancer using real-world data-driven time-inhomogeneous Markov models.iScience · 2026Article
- Temporal Trends in Cause-Specific Cardiovascular Disease Mortality Following Cancer Diagnosis.Cancer medicine · 2026Article
- Multi-Model Machine Learning for Survival Predictions for Castration-Resistant Prostate Cancer.Cancers · 2026Article
- From Spreadsheet To Prediction Tool: A Practical Artificial Intelligence Guide For Urologists.Cureus · 2026Article
- Explainable Machine Learning-Based Overall Survival Classification in Prostate Adenocarcinoma Using Integrated Clinical and Transcriptomic Features.Diagnostics (Basel, Switzerland) · 2026Article
- Early treatment outcome prediction in metastatic castration-resistant prostate cancer utilizing 3-month tumor growth rate (medRxiv : the preprint server for health sciences · 2026Article
- Machine learning approaches for predicting progression in hormone-sensitive prostate cancer patients.Frontiers in oncology · 2026Article
- Unveiling the role of harmonization on clinically significant prostate cancer detection using MRI.Scientific reports · 2025Article
- Prognostic Impact of Time to Castration Resistance on Overall Survival in Patients With Metastatic Castration-Sensitive Prostate Cancer.International journal of urology : official journal of the Japanese Urological Association · 2025Article
- RETRACTED: Machine-Learning-Based Survival Prediction in Castration-Resistant Prostate Cancer: A Multi-Model Analysis Using a Comprehensive Clinical Dataset.Journal of personalized medicine · 2025Article
- Article
- Developing practical machine learning survival models to identify high-risk patients for in-hospital mortality following traumatic brain injury.Scientific reports · 2025Article
- Multi-branch CNNFormer: a novel framework for predicting prostate cancer response to hormonal therapy.Biomedical engineering online · 2024Article
- Comprehensive Analysis of the SUMO-related Signature: Implication for Diagnosis, Prognosis, and Immune Therapeutic Approaches in Cervical Cancer.Biochemical genetics · 2024Article
- Time-dependent personalized prognostic analysis by machine learning in biochemical recurrence after radical prostatectomy: a retrospective cohort study.BMC cancer · 2024Article
- PSA doubling time 4.65 months as an optimal cut-off of Japanese nonmetastatic castration-resistant prostate cancer.Scientific reports · 2024Article
- Identifying the role of MTHFD1L in prostate cancer progression from genetic analysis and experimental validation.American journal of cancer research · 2024Article
- Harnessing machine learning to predict prostate cancer survival: a review.Frontiers in oncology · 2024Review
- Novel research and future prospects of artificial intelligence in cancer diagnosis and treatment.Journal of hematology & oncology · 2023Review
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14 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Machine learning technology is expected to support diagnosis and prognosis prediction in medicine. We used machine learning to construct a new prognostic prediction model for prostate cancer patients based on longitudinal data obtained from age at diagnosis, peripheral blood and urine tests of 340 prostate cancer patients. Random survival forest (RSF) and survival tree were used for machine learning. In the time-series prognostic prediction model for metastatic prostate cancer patients, the RSF model showed better prediction accuracy than the conventional Cox proportional hazards model for almost all time periods of progression-free survival (PFS), overall survival (OS) and cancer-specific survival (CSS). Based on the RSF model, we created a clinically applicable prognostic prediction model using survival trees for OS and CSS by combining the values of lactate dehydrogenase (LDH) before starting treatment and alkaline phosphatase (ALP) at 120 days after treatment. Machine learning provides useful information for predicting the prognosis of metastatic prostate cancer prior to treatment intervention by considering the nonlinear and combined impacts of multiple features. The addition of data after the start of treatment would allow for more precise prognostic risk assessment of patients and would be beneficial for subsequent treatment selection.
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