Artificial intelligence in prostate cancer.
10.1097/CM9.0000000000003689
- Author:
Wei LI
1
;
Ruoyu HU
1
;
Quan ZHANG
1
;
Zhangsheng YU
2
;
Longxin DENG
3
;
Xinhao ZHU
1
;
Yujia XIA
2
;
Zijian SONG
1
;
Alessia CIMADAMORE
4
;
Fei CHEN
5
;
Antonio LOPEZ-BELTRAN
6
;
Rodolfo MONTIRONI
7
;
Liang CHENG
8
;
Rui CHEN
1
Author Information
1. Department of Urology, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200127, China.
2. Department of Bioinformatics and Biostatistics, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai 200240, China.
3. Department of Urology, The First Affiliated Hospital of Naval Medical University (Second Military Medical University), Shanghai 200433, China.
4. Institute of Pathological Anatomy, Department of Medicine, University of Udine, Udine, Italy.
5. Department of Pathology and Laboratory Medicine, New York University Grossman School of Medicine and NYU Langone Health, New York, NY, USA.
6. Department of Surgery, Cordoba University Medical School, Cordoba, Spain.
7. Molecular Medicine and Cell Therapy Foundation, Polytechnic University of the Marche Region, Ancona, Italy.
8. Department of Pathology and Laboratory Medicine, Department of Surgery (Urology), Brown University Warren Alpert Medical School, the Legorreta Cancer Center at Brown University, and Brown University Health, Providence, RI, USA.
- Publication Type:Review
- Keywords:
Artificial intelligence;
Foundation model;
Imaging;
Pathology;
Prostate cancer;
Whole-slide image
- MeSH:
Humans;
Prostatic Neoplasms/diagnosis*;
Male;
Artificial Intelligence;
Deep Learning;
Prognosis
- From:
Chinese Medical Journal
2025;138(15):1769-1782
- CountryChina
- Language:English
-
Abstract:
Prostate cancer (PCa) ranks as the second most prevalent malignancy among men worldwide. Early diagnosis, personalized treatment, and prognosis prediction of PCa play a crucial role in improving patients' survival rates. The advancement of artificial intelligence (AI), particularly the utilization of deep learning (DL) algorithms, has brought about substantial progress in assisting the diagnosis, treatment, and prognosis prediction of PCa. The introduction of the foundation model has revolutionized the application of AI in medical treatment and facilitated its integration into clinical practice. This review emphasizes the clinical application of AI in PCa by discussing recent advancements from both pathological and imaging perspectives. Furthermore, it explores the current challenges faced by AI in clinical applications while also considering future developments, aiming to provide a valuable point of reference for the integration of AI and clinical applications.
