Submit to JCM Review for JCM Propose a Special Issue

Journal Menu

Journal Browser

Prostate Cancer: Recent Advances in Diagnostics and Treatment Planning

Special Issue Editors
Special Issue Information
Keywords
Benefits of Publishing in a Special Issue
Related Special Issue
Published Papers

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

Deadline for manuscript submissions: closed (25 November 2022) | Viewed by 20560

Share This Special Issue

Special Issue Editor

Dr. Theodoros Tokas

E-Mail
Guest Editor

Department of Urology, University General Hospital of Heraklion, University of Crete, Medical School, Heraklion, Greece
Interests: prostate biopsy; prostate cancer; endourology; prostate hyperplasia; urinary stone disease; laparoscopy; robotic surgery
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Prostate cancer (PCa) is the second most frequently occurring malignancy in men worldwide. The aggressiveness of tumors varies, ranging from non-aggressive tumors, which can be safely monitored, to tumors with a poor prognosis, which are only suited to palliative treatment. Using contemporary imaging, biomarkers, nomograms, and precise stratification, particularly of the most clinically heterogeneous portion of intermediate-risk patients, provides a better framework for their management.

Targeted biopsies enhance the diagnosis of clinically significant Pca, as routine transrectal ultrasound is not always reliable. The use of magnetic resonance imaging (MRI) can aid in identifying indications for prostate biopsy, and is fundamental for local staging. When MRI cannot be performed, contemporary, less expensive ultrasound-based methods provide high-quality imaging. More precise staging methods, such as PSMA PET/CT, have been adopted for staging aggressive tumors; however, there is currently insufficient data to support subsequent management.

The early detection and management of PCa can be aided by genetic counseling and germline testing. Biomarkers based on urine, serum, and tissue increase detection and facilitate risk stratification in PCa patients.

All these techniques work together to create risk calculators/nomograms, which may be used to forecast cancer risk, the likelihood of aggressive malignancy, and the likelihood of a good treatment response.

The aim of this Special Issue, published in the Journal of Clinical Medicine, is to provide new insights into PCa, and the advances in diagnostics and treatment planning.

Dr. Theodoros Tokas
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 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
prostate biopsy
prostate imaging
ultrasound
magnetic resonance imaging
MRI
PET scan
biomarkers
nomograms

Benefits of Publishing in a Special Issue

Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
e-Book format: Special Issues with more than 10 articles can be published as dedicated e-books, ensuring wide and rapid dissemination.

Further information on MDPI's Special Issue polices can be found here.

Related Special Issue

Prostate Cancer: Recent Advances in Diagnostics and Treatment Planning—Part II in Journal of Clinical Medicine (4 articles)

Published Papers (9 papers)

Download All Papers

Order results

Result details

Show export options Show export options

Select all

Export citation of selected articles as:

Editorial

Jump to: Research, Review

3 pages, 173 KiB

Open AccessEditorial

Special Issue “Prostate Cancer: Recent Advances in Diagnostics and Treatment Planning”

by Theodoros Tokas

J. Clin. Med. 2022, 11(22), 6823; https://doi.org/10.3390/jcm11226823 - 18 Nov 2022

Cited by 1 | Viewed by 1133

Abstract

This editorial of the Special Issue “Prostate Cancer: Recent Advances in Diagnostics and Treatment Planning” aims to draw more attention to the broad and diverse field of prostate cancer (PCa) diagnosis and the utilization of different diagnostic means to improve clinical decision-making and [...] Read more.

(This article belongs to the Special Issue Prostate Cancer: Recent Advances in Diagnostics and Treatment Planning)

Research

Jump to: Editorial, Review

12 pages, 255 KiB

Open AccessArticle

Determination of Whether Apex or Non-Apex Prostate Cancer Is the Best Candidate for the Use of Prostate-Specific Antigen Density to Predict Pathological Grade Group Upgrading and Upstaging after Radical Prostatectomy

by Cong Huang, Shiming He, Qun He, Yanqing Gong, Gang Song and Liqun Zhou

J. Clin. Med. 2023, 12(4), 1659; https://doi.org/10.3390/jcm12041659 - 19 Feb 2023

Cited by 1 | Viewed by 1760

Abstract

Objective: Previous studies have demonstrated that prostate-specific antigen density (PSAD) may aid in predicting Gleason grade group (GG) upgrading and pathological upstaging in patients with prostate cancer (PCa). However, the differences and associations between patients with apex prostate cancer (APCa) and non-apex prostate cancer (NAPCa) have not been described. The aim of this study was to explore the different roles of PSAD in predicting GG upgrading and pathological upstaging between APCa and NAPCa. Patients and Methods: Five hundred and thirty-five patients who underwent prostate biopsy followed by radical prostatectomy (RP) were enrolled. All patients were diagnosed with PCa and classified as either APCa or NAPCa. Clinical and pathological variables were collected. Univariate, multivariate, and receiver operating characteristic (ROC) analyses were performed. Results: Of the entire cohort, 245 patients (45.8%) had GG upgrading. Multivariate analysis revealed that only PSAD (odds ratio [OR]: 4.149, p < 0.001) was an independent, significant predictor of upgrading. A total of 262 patients (49.0%) had pathological upstaging. Both PSAD (OR: 4.750, p < 0.001) and percentage of positive cores (OR: 5.108, p = 0.002) were independently significant predictors of upstaging. Of the 374 patients with NAPCa, 168 (44.9%) displayed GG upgrading. Multivariate analysis also showed PSAD (OR: 8.176, p < 0.001) was an independent predictor of upgrading. Upstaging occurred in 159 (42.5%) patients with NAPCa, and PSAD (OR: 4.973, p < 0.001) and percentage of positive cores (OR: 3.994, p = 0.034) were independently predictive of pathological upstaging. Conversely, of the 161 patients with APCa, 77 (47.8%) were identified with GG upgrading, and 103 (64.0%) patients with pathological upstaging. Multivariate analysis demonstrated that there were no significant predictors, including PSAD, for predicting GG upgrading (p = 0.462) and pathological upstaging (p = 0.100). Conclusions: PSAD may aid in the prediction of GG upgrading and pathological upstaging in patients with PCa. However, this may only be practical in patients with NAPCa but not with APCa. Additional biopsy cores taken from the prostatic apex region may help improve the accuracy of PSAD in predicting GG upgrading and pathological upstaging after RP. Full article

(This article belongs to the Special Issue Prostate Cancer: Recent Advances in Diagnostics and Treatment Planning)

13 pages, 1404 KiB

Open AccessArticle

Diagnostic Efficiency of Pan-Immune-Inflammation Value to Predict Prostate Cancer in Patients with Prostate-Specific Antigen between 4 and 20 ng/mL

by Meikai Zhu, Yongheng Zhou, Zhifeng Liu, Zhiwen Jiang, Wenqiang Qi, Shouzhen Chen, Wenfu Wang, Benkang Shi and Yaofeng Zhu

J. Clin. Med. 2023, 12(3), 820; https://doi.org/10.3390/jcm12030820 - 19 Jan 2023

Cited by 6 | Viewed by 1827

Abstract

Introduction: To evaluate the predictive value of the pan-immune-inflammation value (PIV) and other systemic inflammatory markers, including the neutrophil-to-lymphocyte ratio (NLR), derived neutrophil-to-lymphocyte ratio (dNLR), monocyte-to-lymphocyte ratio (MLR), platelet-to-lymphocyte ratio (PLR), and systemic immune-inflammation index (SII), for prostate cancer (PCa) and clinically significant prostate cancer (CSPCa) in patients with a prostate-specific antigen (PSA) value between 4 and 20 ng/mL. Patients and Methods: The clinical data of 319 eligible patients who underwent prostate biopsies in our hospital from August 2019 to June 2022 were retrospectively analyzed. CSPCa was defined as a “Gleason grade group of ≥2”. A univariable logistic regression analysis and multivariable logistic regression analysis were conducted to analyze the association between the PIV, SII, MLR, and PCa/CSPCa. For the inflammatory indicators included in the multivariable logistic regression analysis, we constructed models by combining the separate inflammatory indicator and other significant predictors and compared the area under the curve (AUC). A nomogram based on the PIV for PCa was developed. Results: We included 148 PCa patients (including 127 CSPCa patients) and 171 non-PCa patients in total. The patients with PCa were older, had higher MLR, SII, PIV, and total PSA (TPSA) values, consumed more alcohol, and had lower free/total PSA (f/T) values than the other patients. Compared with the non-CSPCa group, the CSPCa group had higher BMI, MLR, PIV, TPSA values, consumed more alcohol, and had lower f/T values. The univariable regression analysis showed that drinking history, higher MLR, PIV, and TPSA values, and lower f/T values were independent predictors of PCa and CSPCa. The AUC of the PIV in the multivariable logistic regression model was higher than those of the MLR and SII. In addition, the diagnostic value of the PIV + PSA for PCa was better than the PSA value. However, the diagnostic value for CSPCa was not significantly different from that of using PSA alone, while the AUC of the PIV + PSA was higher than the individual indicator of the PSA value. Conclusions: Our study suggests that for the patients who were diagnosed with PSA values between 4 and 20 ng/mL, the PIV and MLR are potential indicators for predicting PCa and CSPCa. In addition, our study indicates that the new inflammatory index PIV has clinical value in the diagnosis of PCa and CSPCa. Full article

(This article belongs to the Special Issue Prostate Cancer: Recent Advances in Diagnostics and Treatment Planning)

► Show Figures

Figure 1

13 pages, 1259 KiB

Open AccessArticle

Nomograms Combining PHI and PI-RADS in Detecting Prostate Cancer: A Multicenter Prospective Study

by Yongheng Zhou, Qiang Fu, Zhiqiang Shao, Keqin Zhang, Wenqiang Qi, Shangzhen Geng, Wenfu Wang, Jianfeng Cui, Xin Jiang, Rongyang Li, Yaofeng Zhu, Shouzhen Chen and Benkang Shi

J. Clin. Med. 2023, 12(1), 339; https://doi.org/10.3390/jcm12010339 - 1 Jan 2023

Cited by 2 | Viewed by 1870

Abstract

(1) Background: The study aimed to construct nomograms to improve the detection rates of prostate cancer (PCa) and clinically significant prostate cancer (CSPCa) in the Asian population. (2) Methods: This multicenter prospective study included a group of 293 patients from three hospitals. Univariable and multivariable logistic regression analysis was performed to identify potential risk factors and construct nomograms. Discrimination, calibration, and clinical utility were used to assess the performance of the nomogram. The web-based dynamic nomograms were subsequently built based on multivariable logistic analysis. (3) Results: A total of 293 patients were included in our study with 201 negative and 92 positive results in PCa. Four independent predictive factors (age, prostate health index (PHI), prostate volume, and prostate imaging reporting and data system score (PI-RADS)) for PCa were included, and four factors (age, PHI, PI-RADS, and Log PSA Density) for CSPCa were included. The area under the ROC curve (AUC) for PCa was 0.902 in the training cohort and 0.869 in the validation cohort. The AUC for CSPCa was 0.896 in the training cohort and 0.890 in the validation cohort. (4) Conclusions: The combined diagnosis of PHI and PI-RADS can avoid more unnecessary biopsies and improve the detection rate of PCa and CSPCa. The nomogram with the combination of age, PHI, PV, and PI-RADS could improve the detection of PCa, and the nomogram with the combination of age, PHI, PI-RADS, and Log PSAD could improve the detection of CSPCa. Full article

(This article belongs to the Special Issue Prostate Cancer: Recent Advances in Diagnostics and Treatment Planning)

► Show Figures

Figure 1

10 pages, 548 KiB

Open AccessArticle

Predictors of Clinically Significant Prostate Cancer in Patients with PIRADS Categories 3–5 Undergoing Magnetic Resonance Imaging-Ultrasound Fusion Biopsy of the Prostate

by Stanisław Szempliński, Hubert Kamecki, Małgorzata Dębowska, Bartłomiej Zagożdżon, Mateusz Mokrzyś, Marek Zawadzki, Roman Sosnowski, Andrzej Tokarczyk, Sławomir Poletajew, Piotr Kryst and Łukasz Nyk

J. Clin. Med. 2023, 12(1), 156; https://doi.org/10.3390/jcm12010156 - 25 Dec 2022

Cited by 3 | Viewed by 1919

Abstract

Prostate biopsy is recommended in cases of positive magnetic resonance imaging (MRI), defined as Prostate Imaging Reporting and Data System (PIRADS) category ≥ 3. However, most men with positive MRIs will not be diagnosed with clinically significant prostate cancer (csPC). Our goal was to evaluate pre-biopsy characteristics that influence the probability of a csPC diagnosis in these patients. We retrospectively analyzed 740 consecutive men with a positive MRI and no prior PC diagnosis who underwent MRI-ultrasound fusion biopsies of the prostate in three centers. csPC detection rates (CDRs) for each PIRADS category were calculated. Patient, disease, and lesion characteristics were studied for interdependencies with the csPC diagnosis. The CDR in patients with PIRADS categories 3, 4, and 5 was 10.5%, 30.7%, and 54.6%, respectively. On both uni- and multivariable regression models, older age, being biopsy-naïve, prostate specific antigen ≥ 10 ng/mL, smaller prostate volume, PIRADS > 3, a larger maximum lesion size, a lesion in the peripheral zone, and a positive digital rectal examination were associated with csPC. In this large, multicenter study, we provide new data regarding CDRs in particular PIRADS categories. In addition, we present several strong predictors that further alter the risk of csPC in MRI-positive patients. Our results could help in refining individual risk assessment, especially in PIRADS 3 patients, in whom the risk of csPC is substantially low. Full article

(This article belongs to the Special Issue Prostate Cancer: Recent Advances in Diagnostics and Treatment Planning)

► Show Figures

Figure 1

12 pages, 1293 KiB

Open AccessArticle

Radiomics in PI-RADS 3 Multiparametric MRI for Prostate Cancer Identification: Literature Models Re-Implementation and Proposal of a Clinical–Radiological Model

by Andrea Corsi, Elisabetta De Bernardi, Pietro Andrea Bonaffini, Paolo Niccolò Franco, Dario Nicoletta, Roberto Simonini, Davide Ippolito, Giovanna Perugini, Mariaelena Occhipinti, Luigi Filippo Da Pozzo, Marco Roscigno and Sandro Sironi

J. Clin. Med. 2022, 11(21), 6304; https://doi.org/10.3390/jcm11216304 - 26 Oct 2022

Cited by 5 | Viewed by 2374

Abstract

PI-RADS 3 prostate lesions clinical management is still debated, with high variability among different centers. Identifying clinically significant tumors among PI-RADS 3 is crucial. Radiomics applied to multiparametric MR (mpMR) seems promising. Nevertheless, reproducibility assessment by external validation is required. We retrospectively included all patients with at least one PI-RADS 3 lesion (PI-RADS v2.1) detected on a 3T prostate MRI scan at our Institution (June 2016–March 2021). An MRI-targeted biopsy was used as ground truth. We assessed reproducible mpMRI radiomic features found in the literature. Then, we proposed a new model combining PSA density and two radiomic features (texture regularity (T2) and size zone heterogeneity (ADC)). All models were trained/assessed through 100-repetitions 5-fold cross-validation. Eighty patients were included (26 with GS ≥ 7). In total, 9/20 T2 features (Hector’s model) and 1 T2 feature (Jin’s model) significantly correlated to biopsy on our dataset. PSA density alone predicted clinically significant tumors (sensitivity: 66%; specificity: 71%). Our model obtained a sensitivity of 80% and a specificity of 76%. Standard-compliant works with detailed methodologies achieve comparable radiomic feature sets. Therefore, efforts to facilitate reproducibility are needed, while complex models and imaging protocols seem not, since our model combining PSA density and two radiomic features from routinely performed sequences appeared to differentiate clinically significant cancers. Full article

(This article belongs to the Special Issue Prostate Cancer: Recent Advances in Diagnostics and Treatment Planning)

► Show Figures

Figure 1

7 pages, 1145 KiB

Open AccessArticle

Could 68Ga-PSMA PET/CT Evaluation Reduce the Number of Scheduled Prostate Biopsies in Men Enrolled in Active Surveillance Protocols?

by Pietro Pepe, Marco Roscigno, Ludovica Pepe, Paolo Panella, Marinella Tamburo, Giulia Marletta, Francesco Savoca, Giuseppe Candiano, Sebastiano Cosentino, Massimo Ippolito, Andreas Tsirgiotis and Michele Pennisi

J. Clin. Med. 2022, 11(12), 3473; https://doi.org/10.3390/jcm11123473 - 16 Jun 2022

Cited by 19 | Viewed by 2065

Abstract

Background: To evaluate the accuracy of 68Ga-prostate specific membrane antigen (PSMA) PET/CT in the diagnosis of clinically significant prostate cancer (csPCa) (Grade Group > 2) in men enrolled in Active Surveillance (AS) protocol. Methods: From May 2013 to May 2021, 173 men with very low-risk PCa were enrolled in an AS protocol study. During the follow-up, 38/173 (22%) men were upgraded and 8/173 (4.6%) decided to leave the AS protocol. After four years from confirmatory biopsy (range: 48–52 months), 30/127 (23.6%) consecutive patients were submitted to mpMRI and 68Ga-PSMA PET/CT scan before scheduled repeated biopsy. All the mpMRI (PI-RADS > 3) and 68Ga-PET/TC standardised uptake value (SUVmax) > 5 g/mL index lesions underwent targeted cores (mpMRI-TPBx and PSMA-TPBx) combined with transperineal saturation prostate biopsy (SPBx: median 20 cores). Results: mpMRI and 68Ga-PSMA PET/CT showed 14/30 (46.6%) and 6/30 (20%) lesions suspicious for PCa. In 2/30 (6.6%) men, a csPCa was found; 68Ga-PSMA-TPBx vs. mpMRI-TPBx vs. SPBx diagnosed 1/2 (50%) vs. 1/2 (50%) vs. 2/2 (100%) csPCa, respectively. In detail, mpMRI and 68Ga-PSMA PET/TC demonstrated 13/30 (43.3%) vs. 5/30 (16.7%) false positive and 1 (50%) vs. 1 (50%) false negative results. Conclusion: 68Ga-PSMA PET/CT did not improve the detection for csPCa of SPBx but would have spared 24/30 (80%) scheduled biopsies showing a lower false positive rate in comparison with mpMRI (20% vs. 43.3%) and a negative predictive value of 85.7% vs. 57.1%, respectively. Full article

(This article belongs to the Special Issue Prostate Cancer: Recent Advances in Diagnostics and Treatment Planning)

► Show Figures

Figure 1

Review

Jump to: Editorial, Research

18 pages, 327 KiB

Open AccessReview

A Review of Modern Imaging Landscape for Prostate Cancer: A Comprehensive Clinical Guide

by Paul Gravestock, Bhaskar Kumar Somani, Theodoros Tokas and Bhavan Prasad Rai

J. Clin. Med. 2023, 12(3), 1186; https://doi.org/10.3390/jcm12031186 - 2 Feb 2023

Cited by 3 | Viewed by 2381

Abstract

The development of prostate cancer imaging is rapidly evolving, with many changes to the way patients are diagnosed, staged, and monitored for recurrence following treatment. New developments, including the potential role of imaging in screening and the combined diagnostic and therapeutic applications in the field of theranostics, are underway. In this paper, we aim to outline the current landscape in prostate cancer imaging and look to the future at the potential modalities and applications to come. Full article

(This article belongs to the Special Issue Prostate Cancer: Recent Advances in Diagnostics and Treatment Planning)

12 pages, 950 KiB

Open AccessReview

Role of Deep Learning in Prostate Cancer Management: Past, Present and Future Based on a Comprehensive Literature Review

by Nithesh Naik, Theodoros Tokas, Dasharathraj K. Shetty, B.M. Zeeshan Hameed, Sarthak Shastri, Milap J. Shah, Sufyan Ibrahim, Bhavan Prasad Rai, Piotr Chłosta and Bhaskar K. Somani

J. Clin. Med. 2022, 11(13), 3575; https://doi.org/10.3390/jcm11133575 - 21 Jun 2022

Cited by 11 | Viewed by 3944

Abstract

This review aims to present the applications of deep learning (DL) in prostate cancer diagnosis and treatment. Computer vision is becoming an increasingly large part of our daily lives due to advancements in technology. These advancements in computational power have allowed more extensive and more complex DL models to be trained on large datasets. Urologists have found these technologies help them in their work, and many such models have been developed to aid in the identification, treatment and surgical practices in prostate cancer. This review will present a systematic outline and summary of these deep learning models and technologies used for prostate cancer management. A literature search was carried out for English language articles over the last two decades from 2000–2021, and present in Scopus, MEDLINE, Clinicaltrials.gov, Science Direct, Web of Science and Google Scholar. A total of 224 articles were identified on the initial search. After screening, 64 articles were identified as related to applications in urology, from which 24 articles were identified to be solely related to the diagnosis and treatment of prostate cancer. The constant improvement in DL models should drive more research focusing on deep learning applications. The focus should be on improving models to the stage where they are ready to be implemented in clinical practice. Future research should prioritize developing models that can train on encrypted images, allowing increased data sharing and accessibility. Full article

(This article belongs to the Special Issue Prostate Cancer: Recent Advances in Diagnostics and Treatment Planning)

► Show Figures

Journal Menu

Journal Browser

Prostate Cancer: Recent Advances in Diagnostics and Treatment Planning

Share This Special Issue

Special Issue Editor

Special Issue Information

Keywords

Benefits of Publishing in a Special Issue

Related Special Issue

Published Papers (9 papers)

Editorial

Research

Review

Further Information

Guidelines

MDPI Initiatives

Follow MDPI