Special Issue "The Cutting Edge and Precision Medicine in Prostate Cancer"

A special issue of Journal of Personalized Medicine (ISSN 2075-4426). This special issue belongs to the section "Clinical Medicine, Cell, and Organism Physiology".

Deadline for manuscript submissions: 10 April 2023 | Viewed by 992

Special Issue Editor

Department of Urology, University of Miami Miller School of Medicine, Miami, FL, USA
Interests: machine learning; oncology; S-nitrosylation; cancer biology; immunology; prostate cancer; drug discovery; reproductive urology; molecular endocrinology; endocrine cancer; androgen deprivation therapy

Special Issue Information

Dear Colleagues,

Immune checkpoint inhibition (ICI) with agents such as anti-PD1, anti-PD-L1, and anti-CTLA4 has revolutionized the treatment of many tumor types. Such therapies restore antibody diversity to eliminate tumor cells that previously evaded immune detection. However, ICI is effective only in a subset of patients with prostate cancer. This limited efficacy is believed to result, in part, from tumor heterogeneity and the unique ability of prostate cancer to evolve from androgen-dependent to androgen-independent stages. This evolution involves as-yet-unclear mechanisms in the tumor microenvironment (TME) that promote immune escape under the reduced infiltration of cytotoxic T lymphocytes (CD8+ T cells).

The current Special Issue will focus on culminating and expanding on the knowledge in this area. The topics papers may focus on include (but are not limited to): drug resistance development against prostate cancer, especially against immune checkpoint inhibition; current advancements in the domain of monotherapy as well as combination therapy in suppressing prostate cancer; mechanisms in the tumor microenvironment which modulate resistance development; and past and ongoing trials which focus on overcoming resistance.

Dr. Himanshu Arora
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Journal of Personalized Medicine is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2000 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • immune checkpoint inhibition
  • prostate cancer
  • tumor microenvironment
  • clinical trials in prostate cancer
  • mechanisms in drug resistance
  • immune regulation in prostate cancer

Published Papers (1 paper)

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Research

Article
Generative Adversarial Networks Can Create High Quality Artificial Prostate Cancer Magnetic Resonance Images
J. Pers. Med. 2023, 13(3), 547; https://doi.org/10.3390/jpm13030547 - 18 Mar 2023
Viewed by 812
Abstract
The recent integration of open-source data with machine learning models, especially in the medical field, has opened new doors to studying disease progression and/or regression. However, the ability to use medical data for machine learning approaches is limited by the specificity of data [...] Read more.
The recent integration of open-source data with machine learning models, especially in the medical field, has opened new doors to studying disease progression and/or regression. However, the ability to use medical data for machine learning approaches is limited by the specificity of data for a particular medical condition. In this context, the most recent technologies, like generative adversarial networks (GANs), are being looked upon as a potential way to generate high-quality synthetic data that preserve the clinical variability of a condition. However, despite some success, GAN model usage remains largely minimal when depicting the heterogeneity of a disease such as prostate cancer. Previous studies from our group members have focused on automating the quantitative multi-parametric magnetic resonance imaging (mpMRI) using habitat risk scoring (HRS) maps on the prostate cancer patients in the BLaStM trial. In the current study, we aimed to use the images from the BLaStM trial and other sources to train the GAN models, generate synthetic images, and validate their quality. In this context, we used T2-weighted prostate MRI images as training data for Single Natural Image GANs (SinGANs) to make a generative model. A deep learning semantic segmentation pipeline trained the model to segment the prostate boundary on 2D MRI slices. Synthetic images with a high-level segmentation boundary of the prostate were filtered and used in the quality control assessment by participating scientists with varying degrees of experience (more than ten years, one year, or no experience) to work with MRI images. Results showed that the most experienced participating group correctly identified conventional vs. synthetic images with 67% accuracy, the group with one year of experience correctly identified the images with 58% accuracy, and the group with no prior experience reached 50% accuracy. Nearly half (47%) of the synthetic images were mistakenly evaluated as conventional. Interestingly, in a blinded quality assessment, a board-certified radiologist did not significantly differentiate between conventional and synthetic images in the context of the mean quality of synthetic and conventional images. Furthermore, to validate the usability of the generated synthetic images from prostate cancer MRIs, we subjected these to anomaly detection along with the original images. Importantly, the success rate of anomaly detection for quality control-approved synthetic data in phase one corresponded to that of the conventional images. In sum, this study shows promise that high-quality synthetic images from MRIs can be generated using GANs. Such an AI model may contribute significantly to various clinical applications which involve supervised machine-learning approaches. Full article
(This article belongs to the Special Issue The Cutting Edge and Precision Medicine in Prostate Cancer)
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