Prostate cancer is a disease in which cells in the prostate, a gland in the male reproductive system below the bladder, grow out of control and, among men, it is the second-most frequently diagnosed cancer (other than skin cancer). In recent years, prostate cancer death rate has stabilized and, currently, it is the second-most frequent cause of cancer death in men (after lung cancer). Most deaths occur due to metastasis, as cancer cells from the original tumor establish secondary tumors in distant organs. For a long time, classical cell cultures and animal models have been utilized in basic and applied scientific research, including clinical applications for many diseases, such as prostate cancer, since no better alternatives were available. Although helpful in dissecting cellular mechanisms, these models are poor predictors of physiological behavior mainly because of the lack of appropriate microenvironments. Microfluidics has emerged in the last two decades as a technology that could lead to a paradigm shift in life sciences and, in particular, controlling cancer. Microfluidic systems, such as organ-on-chips, have been assembled to mimic the critical functions of human organs. These microphysiological systems enable the long-term maintenance of cellular co-cultures in vitro to reconstitute in vivo tissue-level microenvironments, bridging the gap between traditional cell cultures and animal models. Several reviews on microfluidics for prostate cancer studies have been published focusing on technology advancement and disease progression. As metastatic castration-resistant prostate cancer remains a clinically challenging late-stage cancer, with no curative treatments, we expanded this review to cover recent microfluidic applications related to prostate cancer research. The review includes discussions of the roles of microfluidics in modeling the human prostate, prostate cancer initiation and development, as well as prostate cancer detection and therapy, highlighting potentially major contributions of microfluidics in the continuous march toward eradicating prostate cancer.
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http://dx.doi.org/10.3390/mi15101195 | DOI Listing |
Ann Nucl Med
January 2025
Department of Biomedical Sciences, Humanitas University, Milan, Italy.
The purpose of this systematic review was to evaluate the role of PSMA PET/CT in intermediate-risk prostate cancer (PCa) patients, to determine whether it could help improve treatment strategy and prognostic stratification. A systematic literature search up to May 2024 was conducted in the PubMed, Embase and Scopus databases. Articles with mixed risk patient populations, review articles, editorials, letters, comments, or case reports were excluded.
View Article and Find Full Text PDFJ Neurooncol
January 2025
Department of Neurosurgery, Allegheny Health Network, Neuroscience Institute, Pittsburgh, PA, United States.
Langenbecks Arch Surg
January 2025
Department for the Promotion of Medical Device Innovation, National Cancer Center Hospital East, 6-5-1, Kashiwanoha, Kashiwa, Chiba, 277-8577, Japan.
Purpose: Assessing surgical skills is vital for training surgeons, but creating objective, automated evaluation systems is challenging, especially in robotic surgery. Surgical procedures generally involve dissection and exposure (D/E), and their duration and proportion can be used for skill assessment. This study aimed to develop an AI model to acquire D/E parameters in robot-assisted radical prostatectomy (RARP) and verify if these parameters could distinguish between novice and expert surgeons.
View Article and Find Full Text PDFInsights Imaging
January 2025
Department of Radiology, the Second Affiliated Hospital of Dalian Medical University, Dalian, 116023, China.
Objective: To evaluate the feasibility of utilizing artificial intelligence (AI)-predicted biparametric MRI (bpMRI) image features for predicting the aggressiveness of prostate cancer (PCa).
Materials And Methods: A total of 878 PCa patients from 4 hospitals were retrospectively collected, all of whom had pathological results after radical prostatectomy (RP). A pre-trained AI algorithm was used to select suspected PCa lesions and extract lesion features for model development.
Mov Disord Clin Pract
January 2025
The Edmond J. Safra Program in Parkinson's Disease, University Health Network and the University of Toronto, Toronto, Ontario, Canada.
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