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Diagnostics
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Optimizing Diagnosis and Defining Predictive and Prognostic Tools in Prostate...
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Optimizing Diagnosis and Defining Predictive and Prognostic Tools in Prostate Cancer

A special issue of Diagnostics (ISSN 2075-4418). This special issue belongs to the section "Pathology and Molecular Diagnostics".

Deadline for manuscript submissions: closed (31 December 2020) | Viewed by 11798

Special Issue Editors

Prof. Martin Andreas Røder

E-Mail Website1 Website2
Guest Editor
Copenhagen Prostate Cancer Center, Department of Urology, Rigshospitalet, Ole Maaloesvej 24, DK-2200 Copenhagen N, Denmark
Interests: prostate cancer; epidemiology; biomarkers; translational research; bladder cancer; kidney transplantation; genomic medicine
Dr. John Thomas Helgstrand

E-Mail Website
Guest Editor
Copenhagen Prostate Cancer Center, Department of Urology, Rigshospitalet, Ole Maaloesvej 24, DK-2200 Copenhagen N, Denmark
Interests: prostate cancer; biomarkers, epidemiology; translational research; biostatistics; urological cancer

Special Issue Information

The optimal pathway for prostate cancer diagnosis is currently under scrutiny as the advent of new technology questions the accuracy of our gold standard method to define men at risk of harboring clinically significant prostate cancer. Namely, the method of systematic transrectal biopsy is currently being challenged by a combination of MRI and transrectal biopsies to reduce overdiagnosis and increase the number of men assigned with the correct diagnosis at first biopsy. Yet more studies are needed to define which men are optimal candidates for pre-biopsy MRI and if further diagnostic workup can be excluded in cases of a normal MRI. Additionally, it remains uncertain if systematic sampling can be omitted for patients with positive MRI and if MRI can replace biopsy sampling in men who are candidates for active surveillance. In men with a diagnosis of prostate cancer, it remains important to define tumor characteristics and find biomarkers that reflect tumor biology. Moreover, biomarkers that accurately predict the response to therapy are urgently needed. We hope this issue will contribute to better understanding of the optimal diagnostic pathway for prostate cancer diagnosis and describe new potential biomarkers for the prediction or prognostication after prostate cancer diagnosis.

Prof. Martin Andreas Røder
Dr. John Thomas Helgstrand
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. Diagnostics 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

  • biomarkers
  • magnetic resonance imaging
  • prostate biopsy
  • MRI fusion biopsy
  • prostate cancer
  • prognostic tools
  • biostatistics

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Published Papers (4 papers)

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Editorial

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5 pages, 221 KiB  
Editorial
High-Risk Prostate Cancer: A Very Challenging Disease in the Field of Uro-Oncology
by Giorgio Napodano, Matteo Ferro and Roberto Sanseverino
Diagnostics 2021, 11(3), 400; https://doi.org/10.3390/diagnostics11030400 - 26 Feb 2021
Cited by 2 | Viewed by 1667
Abstract
Prostate cancer (PCa) is the most common cancer in males and affects 16% of men during their lifetime [...] Full article
(This article belongs to the Special Issue Optimizing Diagnosis and Defining Predictive and Prognostic Tools in Prostate Cancer)

Research

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14 pages, 1892 KiB  
Article
A Multi-Center, Multi-Vendor Study to Evaluate the Generalizability of a Radiomics Model for Classifying Prostate cancer: High Grade vs. Low Grade
by Jose M. Castillo T., Martijn P. A. Starmans, Muhammad Arif, Wiro J. Niessen, Stefan Klein, Chris H. Bangma, Ivo G. Schoots and Jifke F. Veenland
Diagnostics 2021, 11(2), 369; https://doi.org/10.3390/diagnostics11020369 - 22 Feb 2021
Cited by 36 | Viewed by 3953
Abstract
Radiomics applied in MRI has shown promising results in classifying prostate cancer lesions. However, many papers describe single-center studies without external validation. The issues of using radiomics models on unseen data have not yet been sufficiently addressed. The aim of this study is [...] Read more.
Radiomics applied in MRI has shown promising results in classifying prostate cancer lesions. However, many papers describe single-center studies without external validation. The issues of using radiomics models on unseen data have not yet been sufficiently addressed. The aim of this study is to evaluate the generalizability of radiomics models for prostate cancer classification and to compare the performance of these models to the performance of radiologists. Multiparametric MRI, photographs and histology of radical prostatectomy specimens, and pathology reports of 107 patients were obtained from three healthcare centers in the Netherlands. By spatially correlating the MRI with histology, 204 lesions were identified. For each lesion, radiomics features were extracted from the MRI data. Radiomics models for discriminating high-grade (Gleason score ≥ 7) versus low-grade lesions were automatically generated using open-source machine learning software. The performance was tested both in a single-center setting through cross-validation and in a multi-center setting using the two unseen datasets as external validation. For comparison with clinical practice, a multi-center classifier was tested and compared with the Prostate Imaging Reporting and Data System version 2 (PIRADS v2) scoring performed by two expert radiologists. The three single-center models obtained a mean AUC of 0.75, which decreased to 0.54 when the model was applied to the external data, the radiologists obtained a mean AUC of 0.46. In the multi-center setting, the radiomics model obtained a mean AUC of 0.75 while the radiologists obtained a mean AUC of 0.47 on the same subset. While radiomics models have a decent performance when tested on data from the same center(s), they may show a significant drop in performance when applied to external data. On a multi-center dataset our radiomics model outperformed the radiologists, and thus, may represent a more accurate alternative for malignancy prediction. Full article
(This article belongs to the Special Issue Optimizing Diagnosis and Defining Predictive and Prognostic Tools in Prostate Cancer)
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12 pages, 2092 KiB  
Article
AZGP1 Protein Expression in Hormone-Naïve Advanced Prostate Cancer Treated with Primary Androgen Deprivation Therapy
by Mads Dochedahl Winther, Gitte Kristensen, Hein Vincent Stroomberg, Kasper Drimer Berg, Birgitte Grønkær Toft, James D. Brooks, Klaus Brasso and Martin Andreas Røder
Diagnostics 2020, 10(8), 520; https://doi.org/10.3390/diagnostics10080520 - 27 Jul 2020
Cited by 3 | Viewed by 2706
Abstract
Biomarkers for predicting the risk of castration-resistant prostate cancer (CRPC) in men treated with primary androgen deprivation therapy (ADT) are lacking. We investigated whether Zinc-alpha 2 glycoprotein (AZGP1) expression in the diagnostic biopsies of men with hormone-naïve prostate cancer (PCa) undergoing primary ADT [...] Read more.
Biomarkers for predicting the risk of castration-resistant prostate cancer (CRPC) in men treated with primary androgen deprivation therapy (ADT) are lacking. We investigated whether Zinc-alpha 2 glycoprotein (AZGP1) expression in the diagnostic biopsies of men with hormone-naïve prostate cancer (PCa) undergoing primary ADT was predictive of the development of CRPC and PCa-specific mortality. The study included 191 patients who commenced ADT from 2000 to 2011. The AZGP1 expression was evaluated using immunohistochemistry and scored as high or low expression. The risks of CRPC and PCa-specific mortality were analyzed using stratified cumulative incidences and a cause-specific COX regression analysis for competing risk assessment. The median follow-up time was 9.8 (IQR: 6.1–12.7) years. In total, 94 and 97 patients presented with low and high AZGP1 expression, respectively. A low AZGP1 expression was found to be associated with a shorter time to CRPC when compared to patients with a high AZGP1 expression (HR: 1.5; 95% CI: 1.0–2.1; p = 0.03). However, the multivariable analysis demonstrated no added benefit by adding the AZGP1 expression to prediction models for CRPC. No differences for PCa-specific mortality between the AZGP1 groups were observed. In conclusion, a low AZGP1 expression was associated with a shorter time to CRPC for PCa patients treated with first-line ADT but did not add any predictive information besides well-established clinicopathological variables. Full article
(This article belongs to the Special Issue Optimizing Diagnosis and Defining Predictive and Prognostic Tools in Prostate Cancer)
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Review

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17 pages, 767 KiB  
Review
Diagnostic Strategies for Treatment Selection in Advanced Prostate Cancer
by Ciara S. McNevin, Anne-Marie Baird, Ray McDermott and Stephen P. Finn
Diagnostics 2021, 11(2), 345; https://doi.org/10.3390/diagnostics11020345 - 19 Feb 2021
Cited by 19 | Viewed by 2861
Abstract
Prostate Cancer (PCa) is a leading cause of morbidity and mortality among men worldwide. For most men with PCa, their disease will follow an indolent course. However, advanced PCa is associated with poor outcomes. There has been an advent of new therapeutic options [...] Read more.
Prostate Cancer (PCa) is a leading cause of morbidity and mortality among men worldwide. For most men with PCa, their disease will follow an indolent course. However, advanced PCa is associated with poor outcomes. There has been an advent of new therapeutic options with proven efficacy for advanced PCa in the last decade which has improved survival outcomes for men with this disease. Despite this, advanced PCa continues to be associated with a high rate of death. There is a lack of strong evidence guiding the timing and sequence of these novel treatment strategies. This paper focuses on a review of the strategies for diagnostic and the current evidence available for treatment selection in advanced PCa. Full article
(This article belongs to the Special Issue Optimizing Diagnosis and Defining Predictive and Prognostic Tools in Prostate Cancer)
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