Exploring the Use of Artificial Intelligence in the Management of Prostate Cancer.

This review aims to explore the current state of research on the use of artificial intelligence (AI) in the management of prostate cancer. We examine the various applications of AI in prostate cancer, including image analysis, prediction of treatment outcomes, and patient stratification. Additionally, the review will evaluate the current limitations and challenges faced in the implementation of AI in prostate cancer management. Recent literature has focused particularly on the use of AI in radiomics, pathomics, the evaluation of surgical skills, and patient outcomes. AI has the potential to revolutionize the future of prostate cancer management by improving diagnostic accuracy, treatment planning, and patient outcomes. Studies have shown improved accuracy and efficiency of AI models in the detection and treatment of prostate cancer, but further research is needed to understand its full potential as well as limitations.

Chu TN ，Wong EY ，Ma R ，Yang CH ，Dalieh IS ，Hung AJ ... -  《-》

Radiomics and artificial intelligence in prostate cancer: new tools for molecular hybrid imaging and theragnostics.

In prostate cancer (PCa), the use of new radiopharmaceuticals has improved the accuracy of diagnosis and staging, refined surveillance strategies, and introduced specific and personalized radioreceptor therapies. Nuclear medicine, therefore, holds great promise for improving the quality of life of PCa patients, through managing and processing a vast amount of molecular imaging data and beyond, using a multi-omics approach and improving patients' risk-stratification for tailored medicine. Artificial intelligence (AI) and radiomics may allow clinicians to improve the overall efficiency and accuracy of using these "big data" in both the diagnostic and theragnostic field: from technical aspects (such as semi-automatization of tumor segmentation, image reconstruction, and interpretation) to clinical outcomes, improving a deeper understanding of the molecular environment of PCa, refining personalized treatment strategies, and increasing the ability to predict the outcome. This systematic review aims to describe the current literature on AI and radiomics applied to molecular imaging of prostate cancer.

Liberini V ，Laudicella R ，Balma M ，Nicolotti DG ，Buschiazzo A ，Grimaldi S ，Lorenzon L ，Bianchi A ，Peano S ，Bartolotta TV ，Farsad M ，Baldari S ，Burger IA ，Huellner MW ，Papaleo A ，Deandreis D ... -  《-》

Artificial Intelligence and Pathomics: Prostate Cancer.

Artificial intelligence (AI) has the potential to transform pathologic diagnosis and cancer patient management as a predictive and prognostic biomarker. AI-based systems can be used to examine digitally scanned histopathology slides and differentiate benign from malignant cells and low from high grade. Deep learning models can analyze patient data from individual or multimodal combinations and identify patterns to be used to predict the response to different therapeutic options, the risk of recurrence or progression, and the prognosis of the newly diagnosed patient. AI-based models will improve treatment planning for patients with prostate cancer and improve the efficiency and cost-effectiveness of the pathology laboratory.

Azadi Moghadam P ，Bashashati A ，Goldenberg SL 《-》

The Novel Green Learning Artificial Intelligence for Prostate Cancer Imaging: A Balanced Alternative to Deep Learning and Radiomics.

The application of artificial intelligence (AI) on prostate magnetic resonance imaging (MRI) has shown promising results. Several AI systems have been developed to automatically analyze prostate MRI for segmentation, cancer detection, and region of interest characterization, thereby assisting clinicians in their decision-making process. Deep learning, the current trend in imaging AI, has limitations including the lack of transparency "black box", large data processing, and excessive energy consumption. In this narrative review, the authors provide an overview of the recent advances in AI for prostate cancer diagnosis and introduce their next-generation AI model, Green Learning, as a promising solution.

Kaneko M ，Magoulianitis V ，Ramacciotti LS ，Raman A ，Paralkar D ，Chen A ，Chu TN ，Yang Y ，Xue J ，Yang J ，Liu J ，Jadvar DS ，Gill K ，Cacciamani GE ，Nikias CL ，Duddalwar V ，Jay Kuo CC ，Gill IS ，Abreu AL ... -  《-》

[Research Progress of Artificial Intelligence in Prostate Cancer Diagnosis Application].

Hong S ，Zhang H 《-》