Future of prostate imaging: Artificial intelligence in assessing prostatic magnetic resonance imaging

Revolutionizing prostate cancer therapy: Artificial intelligence - Based nanocarriers for precision diagnosis and treatment

Prostate cancer is one of the major health challenges in the world and needs novel therapeutic approaches to overcome the limitations of conventional treatment. This review delineates the transformative potential of artificial intelligence (AL) in enhancing nanocarrier-based drug delivery systems for prostate cancer therapy. With its ability to optimize nanocarrier design and predict drug delivery kinetics, AI has revolutionized personalized treatment planning in oncology. We discuss how AI can be integrated with nanotechnology to address challenges related to tumor heterogeneity, drug resistance, and systemic toxicity. Emphasis is placed on strong AI-driven advancements in the design of nanocarriers, structural optimization, targeting of ligands, and pharmacokinetics. We also give an overview of how AI can better predict toxicity, reduce costs, and enable personalized medicine. While challenges persist in the way of data accessibility, regulatory hurdles, and interactions with the immune system, future directions based on explainable AI (XAI) models, integration of multimodal data, and green nanocarrier designs promise to move the field forward. Convergence between AI and nanotechnology has been one key step toward safer, more effective, and patient-tailored cancer therapy.

Optimizing Multiparametric MRI Protocols for Prostate Cancer Detection: A Comprehensive Assessment Aligned with PI-RADS Guidelines

Background and Aim

Multiparametric magnetic resonance imaging (mpMRI) is recognized as the most indicative method for diagnosing prostate cancer. The purpose of this narrative review is to provide a comprehensive evaluation aligned with the Prostate Imaging and Reporting Data System (PI-RADS) guidelines, offering an in-depth insight into the various MRI sequences used in a standard mpMRI protocol. Additionally, it outlines the critical technical requirements necessary to perform a standard mpMRI examination of the prostate, as defined by the PI-RADS specifications.

Methods

European Society of Urogenital Radiology has released PI-RADS guideline detailing its suggestions aimed at improving the standards of the procedure. The purpose of this guideline is to establish a standard strategy for MRI protocols and image interpretation, aiming to prevent variability in each of the imaging and interpretation stages.

Results

A standard mpMRI protocol comprises morphological sequences and functional sequences. Morphological sequences which encompass T1- and T2-weighted images, and various functional sequences include diffusion-weighted imaging, and dynamic contrast-enhanced MRI. The PI-RADS recommendations assert that having a standard and uniform protocol for all MRI centers is imperative. Furthermore, the existence of a standardized checklist for interpreting MRI images can foster greater consensus in the process of diagnosing and treating patients.

Conclusion

Standardized protocols and checklists for mpMRI interpretation are essential for achieving greater consensus among radiologists, ultimately leading to improved diagnostic outcomes in prostate cancer.

A Comprehensive Review of Artificial Intelligence in Prostate Cancer Care: State-of-the-Art Diagnostic Tools and Future Outlook

Prostate cancer remains a significant global health challenge, characterized by high incidence and substantial morbidity and mortality rates. Early detection is critical for improving patient outcomes, yet current diagnostic methods have limitations in accuracy and reliability. Artificial intelligence (AI) has emerged as a promising tool to address these challenges in prostate cancer care. AI technologies, including machine learning algorithms and advanced imaging techniques, offer potential solutions to enhance diagnostic accuracy, optimize treatment strategies, and personalize patient care. This review explores the current landscape of AI applications in prostate cancer diagnostics, highlighting state-of-the-art tools and their clinical implications. By synthesizing recent advancements and discussing future directions, the review underscores the transformative potential of AI in revolutionizing prostate cancer diagnosis and management. Ultimately, integrating AI into clinical practice can potentially improve outcomes and quality of life for patients affected by prostate cancer.

[68Ga]PSMA-11 for positron emission tomography (PET) imaging of prostate-specific membrane antigen (PSMA)-positive lesions in men with prostate cancer

INTRODUCTION

Theranostics targeting prostate-specific membrane antigen (PSMA) represent a new targeted approach for prostate cancer care that combines diagnostic and therapeutic radiopharmaceuticals to diagnose and treat the disease. Positron emission tomography (PET) is the imaging method of choice and several diagnostic radiopharmaceuticals for quantifying PSMA have received FDA approval and are in clinical use. [68Ga]Ga-PSMA-11 is one such imaging agent and the focus of this article. One beta-emitting radioligand therapy ([177Lu]Lu-PSMA-617) has also received FDA approval for prostate cancer treatment, and several other alpha- and beta-emitting radioligand therapies are in clinical trials.

AREAS COVERED

Theranostics targeting PSMA in men with prostate cancer are discussed with a focus on use of [68Ga]Ga-PSMA-11 for imaging PSMA-positive lesions in men with prostate cancer. The review covers [68Ga]Ga-PSMA-11 manufacture, current regulatory status, comparison of [68Ga]Ga-PSMA-11 to other imaging techniques, clinical updates, and emerging applications of artificial intelligence for [68Ga]Ga-PSMA-11 PET.

EXPERT OPINION

[68Ga]Ga-PSMA-11 is used in conjunction with a PET/CT scan to image PSMA positive lesions in men with prostate cancer. It is manufactured by chelating precursor with68Ga, either from a generator or cyclotron, and has regulatory approval around the world. It is widely used clinically in conjunction with radioligand therapies like [177Lu]Lu-PSMA-617.

Artificial intelligence and allied subsets in early detection and preclusion of gynecological cancers

Gynecological cancers including breast, cervical, ovarian, uterine, and vaginal, pose the greatest threat to world health, with early identification being crucial to patient outcomes and survival rates. The application of machine learning (ML) and artificial intelligence (AI) approaches to the study of gynecological cancer has shown potential to revolutionize cancer detection and diagnosis. The current review outlines the significant advancements, obstacles, and prospects brought about by AI and ML technologies in the timely identification and accurate diagnosis of different types of gynecological cancers. The AI-powered technologies can use genomic data to discover genetic alterations and biomarkers linked to a particular form of gynecologic cancer, assisting in the creation of targeted treatments. Furthermore, it has been shown that the potential benefits of AI and ML technologies in gynecologic tumors can greatly increase the accuracy and efficacy of cancer diagnosis, reduce diagnostic delays, and possibly eliminate the need for needless invasive operations. In conclusion, the review focused on the integrative part of AI and ML based tools and techniques in the early detection and exclusion of various cancer types; together with a collaborative coordination between research clinicians, data scientists, and regulatory authorities, which is suggested to realize the full potential of AI and ML in gynecologic cancer care.