Thresholds of Body Composition Changes Associated with Survival During Androgen Deprivation Therapy in Prostate Cancer DOI Creative Commons

P.Y. Chen,

Pai-Kai Chiang,

Jhen‐Bin Lin

et al.

European Urology Open Science, Journal Year: 2024, Volume and Issue: 70, P. 99 - 108

Published: Oct. 23, 2024

Language: Английский

Comparing the Effectiveness of Artificial Intelligence Models in Predicting Ovarian Cancer Survival: A Systematic Review DOI Creative Commons
Farkhondeh Asadi, Milad Rahimi, Nahid Ramezanghorbani

et al.

Cancer Reports, Journal Year: 2025, Volume and Issue: 8(3)

Published: March 1, 2025

ABSTRACT Background This systematic review investigates the use of machine learning (ML) algorithms in predicting survival outcomes for ovarian cancer (OC) patients. Key prognostic endpoints, including overall (OS), recurrence‐free (RFS), progression‐free (PFS), and treatment response prediction (TRP), are examined to evaluate effectiveness these identify significant features that influence predictive accuracy. Recent Findings A thorough search four major databases—PubMed, Scopus, Web Science, Cochrane—resulted 2400 articles published within last decade, with 32 studies meeting inclusion criteria. Notably, most publications emerged after 2021. Commonly used included random forest, support vector machines, logistic regression, XGBoost, various deep models. Evaluation metrics such as area under curve (AUC) (18 studies), concordance index (C‐index) (11 accuracy studies) were frequently employed. Age at diagnosis, tumor stage, CA‐125 levels, treatment‐related factors consistently highlighted predictors, emphasizing their relevance OC prognosis. Conclusion ML models demonstrate considerable potential outcomes; however, challenges persist regarding model interpretability. Incorporating diverse data types—such clinical, imaging, molecular datasets—holds promise enhancing capabilities. Future advancements will depend on integrating heterogeneous sources multimodal approaches, which crucial improving precision OC.

Language: Английский

Citations

1
Identifying threshold of CT-defined muscle loss after radiotherapy for survival in oral cavity cancer using machine learning DOI
Jie Lee,

Jhen‐Bin Lin,

Wan-Chun Lin

et al.

European Radiology, Journal Year: 2024, Volume and Issue: unknown

Published: Dec. 20, 2024

Language: Английский

Citations

2
Thresholds of Body Composition Changes Associated with Survival During Androgen Deprivation Therapy in Prostate Cancer DOI Creative Commons

P.Y. Chen,

Pai-Kai Chiang,

Jhen‐Bin Lin

et al.

European Urology Open Science, Journal Year: 2024, Volume and Issue: 70, P. 99 - 108

Published: Oct. 23, 2024

Language: Английский

Citations

0