Chinese Medical Journal study highlights the role of artificial intelligence in prostate cancer management

Cancer is a significant global health issue affecting millions worldwide. Among the different types of cancer, prostate cancer (PCa)—which affects the prostate gland in the male reproductive system—is the second most prevalent type of cancer among men worldwide. Early screening and treatment are critical to reduce deaths associated with PCa but it is quite challenging due to the absence of symptoms in its early stages. Recent advancements in artificial intelligence (AI) are a boon for clinicians since it enables accurate detection of diseases even with limited signs.

Recently, a team of researchers led by Dr. Liang Cheng from Brown University Warren Alpert Medical School, United States of America, and Dr. Rui Chen from Shanghai Jiao Tong University School of Medicine, China, conducted a review to identify different AI-based models used in the diagnosis and treatment of PCa. The review was published online in the Chinese Medical Journal on July 09, 2025.

“In this review, we focus on AI-assisted personalized management and precision medi­cine for PCa patients from the aspects of pathology and imaging; summarize the cutting-edge advances of AI in PCa diagnosis, treatment, patient prognosis prediction, and molecular subtyping; and discuss the application of foundation model in PCa,” says Dr. Cheng.

Like any other cancer, detection of PCa involves a combination of tests and physical examinations. Notably, prostate-specific antigen (PSA) test and digital rectal exam are commonly used screening tools often coupled with different imaging techniques. While PSA tests are promising for early detection, only using PSA screening often leads to overdiagnosis and unnecessary biopsies, calling for better diagnostic interventions. Asian Prostate Cancer Artificial Intelligence is one such model that uses multimodal clinical parameters to optimize screening and reduce the number of unnecessary biopsies.

Galen Prostate is another AI-based model with convolutional neural networks (CNNs) for detection of PCa. Although used after the confirmation of cancer, this model helps identify the aggressiveness of the cancer cells by optimizing Gleason grading system, a part of biopsy screening which grades the stage of PCa based on pathological classification. Additionally, imaging tools like Fuzzy C-Means clustering algorithm for magnetic resonance imaging (MRI) analysis help in differentiating between cancerous and non-cancerous tumors while ProGNet and CNN-Based MRI Segmentation Models help in identifying and outlining abnormal tissue areas (lesions). Such interventions help save time and enable early and precise detection of PCa.

A critical step in managing PCa is therapy management. Each patient suffers from a different stage of cancer where the treatment needs to be tailored according to their needs. Localized PCa are usually treated with a combination of androgen deprivation therapy (ADT) and radiotherapy. While ADT does benefit some patients, it affects the quality of life of others. Multimodal Artificial Intelligence Prostate Prognostic Model is an AI model that helps identify patients who can benefit from short-term ADT while ruling out those unlikely to respond, assisting the clinicians in personalized treatment decisions.

The study also discusses other AI based models that improve precision in radiation therapy for example, random forest-based model for radiotherapy which automates the treatment parameters and Virtual Treatment Planner which enhances radiotherapy planning by optimizing treatment parameters. Additionally, there are various smart algorithms that help assess the outcome (prognosis) of the condition. One such model is Survival Quilt that provides optimized 10-year survival predictions for patients with localized PCa.

Biochemical recurrence (BCR) signifies the recurrence of PCa after treatment while metastasis marks the spread of cancer to different organs. Tools like Prostate Cancer Lymph Node Metastasis Detector and XGBoost have been reported for their accuracy in metastasis detection and BCR predictions, respectively. Lymph Node Metastases Diagnostic Model is an advanced model that can also identify micrometastases in lymph nodes.

The review also analyzes the switch of traditional AI tools with foundational tools. Foundational tools are the futuristic, versatile AI models which train on a large set of data and can perform multiple tasks at a time. While traditional task-specific AI tools have significantly improved patient care, the advent of foundational tools marks a groundbreaking shift in healthcare.

Overall, the study provides a complete update on how AI is transforming clinical practice. While the advancements show a significant success, researchers also highlight the challenges for implementation of such tools, emphasizing on further research strategies to reduce the large data requirements and AI bias.

“In the future, as databases become more robust, algorithms are further refined, and supportive laws and regulations are developed, AI is poised to play an even more transformative role in precision medicine for PCa,” concludes Dr. Chen.
 

***

Reference
DOI: 10.1097/CM9.0000000000003689