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Clinical Imaging and Newest Therapies for Prostate Cancer
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Clinical Imaging and Newest Therapies for Prostate Cancer

A special issue of Journal of Clinical Medicine (ISSN 2077-0383). This special issue belongs to the section "Nephrology & Urology".

Deadline for manuscript submissions: 15 September 2024 | Viewed by 1666

Special Issue Editors

Dr. Patrick Julien Treacy

E-Mail Website
Guest Editor
1. RPA Institute of Academic Surgery (IAS), Royal Prince Alfred Hospital and University of Sydney, Missenden Road, PO Box M40, Sydney, NSW 2050, Australia
2. Department of Urology, Nice University Hospital, 06000 Nice, France
3. Department of Urology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
Interests: uro-oncology; imaging; prostate cancer; kidney cancer; robotic surgery
Dr. Ruban Thanigasalam

E-Mail Website
Guest Editor
RPA Institute of Academic Surgery (IAS), Royal Prince Alfred Hospital and University of Sydney, Missenden Road, PO Box M40, Sydney, NSW 2050, Australia
Interests: prostate cancer; kidney cancer; uro-oncology; robotic surgery
Dr. Scott Leslie

E-Mail Website
Guest Editor
RPA Institute of Academic Surgery (IAS), Royal Prince Alfred Hospital and University of Sydney, Missenden Road, PO Box M40, Sydney, NSW 2050, Australia
Interests: prostate cancer; bladder cancer; kidney cancer; uro-oncology; robotic surgery

Special Issue Information

Dear Colleagues,

Clinical imaging and the latest therapies for prostate cancer have witnessed remarkable advancements, revolutionizing the diagnosis and treatment landscape. State-of-the-art imaging techniques, such as microscopy imaging (multiphoton, confocal, and microultrasound), as well as in vivo imaging techniques, have significantly improved diagnostic accuracy, enabling the detection of early stage prostate cancer and enhancing disease monitoring.

Moreover, novel therapeutic approaches are reshaping prostate cancer treatment. Innovative therapies, including targeted treatments such as PARP inhibitors, are offering promising outcomes and improved survival rates for patients. Additionally, advances in minimally invasive techniques and robotic-assisted surgeries have reduced the invasiveness of treatments and enhanced patient recovery, and focal therapies are finding a new way in with clinical trials using new laser ablation techniques.

In this Special Issue, we invite authors to submit papers on the clinical imaging and newest therapies for prostate cancer.

Dr. Patrick Julien Treacy
Dr. Ruban Thanigasalam
Dr. Scott Leslie
Guest Editors

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 Clinical Medicine is an international peer-reviewed open access semimonthly 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 2600 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

  • prostate
  • cancer
  • multiphoton
  • PSMA
  • MRI
  • lymphadenectomy

Published Papers (2 papers)

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Research

8 pages, 222 KiB  
Article
Predictors of Metastasis in 68GA-Prostate Specific Membrane Antigen Pet-CT in the Primary Staging of Prostate Cancer
by Erkin Karaca, Erdem Kisa, Mehmet Caglar Cakici, Taha Cetin, Mehmet Yigit Yalcin, Mert Hamza Ozbilen, Cagdas Bildirici and Gokhan Koc
J. Clin. Med. 2024, 13(10), 2774; https://doi.org/10.3390/jcm13102774 - 8 May 2024
Viewed by 296
Abstract
Background: The objective of this study was to investigate factors influencing Gallium 68 Prostate Specific Membrane Antigen Positron Emission Tomography (Ga68 PSMA PET-CT) uptake for primary staging in prostate cancer. Methods: Retrospective analysis was conducted on 499 non-metastatic and 243 de [...] Read more.
Background: The objective of this study was to investigate factors influencing Gallium 68 Prostate Specific Membrane Antigen Positron Emission Tomography (Ga68 PSMA PET-CT) uptake for primary staging in prostate cancer. Methods: Retrospective analysis was conducted on 499 non-metastatic and 243 de novo metastatic prostate cancer cases undergoing Ga68 PSMA PET-CT. Demographic, clinical, and imaging data were collected and analyzed. Multivariate logistic regression determined independent risk factors for metastasis detection on Ga68 PSMA PET-CT. Results: Metastatic cases showed higher levels of total PSA, PSA density (dPSA) and biopsy ISUP grade group compared to non-metastatic cases. Multivariate analysis identified cT2 stage and dPSA as independent predictors of metastasis detection on Ga68 PSMA PET-CT. Conclusions: Ga68 PSMA PET-CT plays a crucial role in prostate cancer staging, with identified factors such as clinical T stage and dPSA significantly impacting its diagnostic accuracy. These findings underscore the importance of Ga68 PSMA PET-CT in refining clinical staging and guiding treatment decisions for prostate cancer patients. Full article
(This article belongs to the Special Issue Clinical Imaging and Newest Therapies for Prostate Cancer)
11 pages, 2341 KiB  
Article
Multi-Institutional Development and Validation of a Radiomic Model to Predict Prostate Cancer Recurrence Following Radical Prostatectomy
by Linda My Huynh, Benjamin Bonebrake, Joshua Tran, Jacob T. Marasco, Thomas E. Ahlering, Shuo Wang and Michael J. Baine
J. Clin. Med. 2023, 12(23), 7322; https://doi.org/10.3390/jcm12237322 - 26 Nov 2023
Viewed by 995
Abstract
The use of multiparametric magnetic resonance imaging (mpMRI)-derived radiomics has the potential to offer noninvasive, imaging-based biomarkers for the identification of subvisual characteristics indicative of a poor oncologic outcome. The present study, therefore, seeks to develop, validate, and assess the performance of an [...] Read more.
The use of multiparametric magnetic resonance imaging (mpMRI)-derived radiomics has the potential to offer noninvasive, imaging-based biomarkers for the identification of subvisual characteristics indicative of a poor oncologic outcome. The present study, therefore, seeks to develop, validate, and assess the performance of an MRI-derived radiomic model for the prediction of prostate cancer (PC) recurrence following radical prostatectomy (RP) with curative intent. mpMRI imaging was obtained from 251 patients who had undergone an RP for the treatment of localized prostate cancer across two institutions and three surgeons. All patients had a minimum of 2 years follow-up via prostate-specific antigen serum testing. Each prostate mpMRI was individually reviewed, and the prostate was delineated as a single slice (ROI) on axial T2 high-resolution image sets. A total of 924 radiomic features were extracted and tested for stability via intraclass correlation coefficient (ICC) following image normalization via histogram matching. Fourteen important and nonredundant features were found to be predictors of PC recurrence at a mean ± SD of 3.2 ± 2.2 years post-RP. Five-fold, ten-run cross-validation of the model containing these fourteen features yielded an area under the curve (AUC) of 0.89 ± 0.04 in the training set (n = 225). In comparison, the University of California San Fransisco Cancer of the Prostate Risk Assessment score (UCSF-CAPRA) and Memorial Sloan Kettering Cancer Center (MSKCC) Pre-Radical prostatectomy nomograms yielded AUC of 0.66 ± 0.05 and 0.67 ± 0.05, respectively (p < 0.01). When the radiomic model was applied to the test set (n = 26), AUC was 0.78; sensitivity, specificity, positive predictive value, and negative predictive value were 60%, 86%, 52%, and 89%, respectively. Accuracy in predicting PC recurrence was 81%. Full article
(This article belongs to the Special Issue Clinical Imaging and Newest Therapies for Prostate Cancer)
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