Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
4.7
3.0
Journals
Diagnostics
Special Issues
Diagnostic Imaging of Prostate Cancer
share announcement

Diagnostic Imaging of Prostate Cancer

A special issue of Diagnostics (ISSN 2075-4418). This special issue belongs to the section "Medical Imaging and Theranostics".

Deadline for manuscript submissions: 31 May 2025 | Viewed by 3181

Special Issue Editor

Dr. Po-Fan Hsieh

E-Mail Website
Guest Editor
1. Department of Urology, China Medical University Hospital, Taichung 404, Taiwan
2. School of Medicine, China Medical University, Taichung 404, Taiwan
Interests: prostate cancer; minimal invasive surgery; focal therapy; targeted biopsy; urolithiasis
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue focuses on the transformative role of imaging technologies in the diagnosis, staging, and management of prostate cancer. Featuring the latest research and innovations, it explores how advanced imaging modalities such as MRI, PET, and multimodal imaging are enhancing our ability to detect tumors early, assess their aggressiveness, and guide precision treatment plans. With a multidisciplinary approach, the contributions in this issue aim to improve patient outcomes and advance the field of prostate cancer imaging.

Dr. Po-Fan Hsieh
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. 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

  • prostate cancer imaging
  • multiparametric MRI
  • PET/CT in prostate cancer
  • targeted radionuclide imaging
  • advanced imaging techniques
  • image-guided interventions for prostate cancer

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 policies can be found here.

Published Papers (4 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

Jump to: Other

17 pages, 2449 KiB  
Article
Comparing and Combining Artificial Intelligence and Spectral/Statistical Approaches for Elevating Prostate Cancer Assessment in a Biparametric MRI: A Pilot Study
by Rulon Mayer, Yuan Yuan, Jayaram Udupa, Baris Turkbey, Peter Choyke, Dong Han, Haibo Lin and Charles B. Simone II
Diagnostics 2025, 15(5), 625; https://doi.org/10.3390/diagnostics15050625 - 5 Mar 2025
Viewed by 619
Abstract
Background: Prostate cancer management optimally requires non-invasive, objective, quantitative, accurate evaluation of prostate tumors. The current research applies visual inspection and quantitative approaches, such as artificial intelligence (AI) based on deep learning (DL), to evaluate MRI. Recently, a different spectral/statistical approach has been [...] Read more.
Background: Prostate cancer management optimally requires non-invasive, objective, quantitative, accurate evaluation of prostate tumors. The current research applies visual inspection and quantitative approaches, such as artificial intelligence (AI) based on deep learning (DL), to evaluate MRI. Recently, a different spectral/statistical approach has been used to successfully evaluate spatially registered biparametric MRIs for prostate cancer. This study aimed to further assess and improve the spectral/statistical approach through benchmarking and combination with AI. Methods: A zonal-aware self-supervised mesh network (Z-SSMNet) was applied to the same 42-patient cohort from previous spectral/statistical studies. Using the probability of clinical significance of prostate cancer (PCsPCa) and a detection map, the affiliated tumor volume, eccentricity was computed for each patient. Linear and logistic regression were applied to the International Society of Urological Pathology (ISUP) grade and PCsPCa, respectively. The R, p-value, and area under the curve (AUROC) from the Z-SSMNet output were computed. The Z-SSMNet output was combined with the spectral/statistical output for multiple-variate regression. Results: The R (p-value)–AUROC [95% confidence interval] from the Z-SSMNet algorithm relating ISUP to PCsPCa is 0.298 (0.06), 0.50 [0.08–1.0]; relating it to the average blob volume, it is 0.51 (0.0005), 0.37 [0.0–0.91]; relating it to total tumor volume, it is 0.36 (0.02), 0.50 [0.0–1.0]. The R (p-value)–AUROC computations showed a much poorer correlation for eccentricity derived from the Z-SSMNet detection map. Overall, DL/AI showed poorer performance relative to the spectral/statistical approaches from previous studies. Multi-variable regression fitted AI average blob size and SCR results at a level of R = 0.70 (0.000003), significantly higher than the results for the univariate regression fits for AI and spectral/statistical approaches alone. Conclusions: The spectral/statistical approaches performed well relative to Z-SSMNet. Combining Z-SSMNet with spectral/statistical approaches significantly enhanced tumor grade prediction, possibly providing an alternative to current prostate tumor assessment. Full article
(This article belongs to the Special Issue Diagnostic Imaging of Prostate Cancer)
►▼ Show Figures

Figure 1

11 pages, 438 KiB  
Article
Large Italian Multicenter Study on Prognostic Value of Baselines Variables in mCRPC Patients Treated with 223RaCl2: Ten Years of Clinical Experience
by Maria Silvia De Feo, Luca Filippi, Matteo Bauckneht, Elisa Lodi Rizzini, Cristina Ferrari, Valentina Lavelli, Andrea Marongiu, Gianmario Sambuceti, Claudia Battisti, Antonio Mura, Giuseppe Fornarini, Sara Elena Rebuzzi, Alessio Farcomeni, Alessandra Murabito, Susanna Nuvoli, Miriam Conte, Melissa Montebello, Renato Patrizio Costa, Arber Golemi, Manlio Mascia, Laura Travascio, Fabio Monari, Giuseppe Rubini, Angela Spanu, Giuseppe De Vincentis and Viviana Frantellizziadd Show full author list remove Hide full author list
Diagnostics 2025, 15(3), 339; https://doi.org/10.3390/diagnostics15030339 - 31 Jan 2025
Viewed by 701
Abstract
Background/Objectives: The prognostic value of baseline clinical parameters in predicting the survival prolonging effect of Radium-223-dichloride (223RaCl2) for metastatic castration resistant prostate cancer (mCRPC) patients has been the object of intensive research and remains an open issue. This national [...] Read more.
Background/Objectives: The prognostic value of baseline clinical parameters in predicting the survival prolonging effect of Radium-223-dichloride (223RaCl2) for metastatic castration resistant prostate cancer (mCRPC) patients has been the object of intensive research and remains an open issue. This national multicenter study aimed to corroborate the evidence of ten years of clinical experience with 223RaCl2 by collecting data from eight Italian Nuclear Medicine Units. Methods: Data from 581 consecutive mCRPC patients treated with 223RaCl2 were retrospectively analyzed. Several baseline variables relevant to the overall survival (OS) analysis were considered, including age, previous radical prostatectomy/radiotherapy, number of previous treatment lines, prior chemotherapy, Gleason score, presence of lymphoadenopaties, number of bone metastases, concomitant use of bisphosphonates/Denosumab, Eastern Cooperative Oncology Group Performance Status (ECOG-PS), as well as baseline values of hemoglobin (Hb), platelets, Total Alkaline Phosphatase (tALP), Lactate Dehydrogenase (LDH), and Prostate-Specific Antigen (PSA). Data were summarized using descriptive statistics, univariate analysis and multivariate analysis with the Cox model. Results: The median OS time was 14 months (95%CI 12–17 months). At univariate analysis age, the number of previous treatment lines, number of bone metastases, ECOG-PS, presence of lymphadenopathies at the time of enrollment, as well as baseline tALP, PSA, and Hb, were independently associated with OS. After multivariate analysis, the number of previous treatment lines (HR = 1.1670, CI = 1.0095–1.3491, p = 0.0368), the prior chemotherapy (HR = 0.6461, CI = 0.4372–0.9549, p = 0.0284), the presence of lymphadenopathies (HR = 1.5083, CI = 1.1210–2.0296, p = 0.0066), the number of bone metastases (HR = 0.6990, CI = 0.5416–0.9020, p = 0.0059), ECOG-PS (HR = 1.3551, CI = 1.1238–1.6339, p = 0.0015), and baseline values of tALP (HR = 1.0008, CI = 1.0003–1.0013, p = 0.0016) and PSA (HR = 1.0004, CI = 1.0002–1.0006, p = 0.0005) remained statistically significant. Conclusions: In the era of precision medicine and in the landscape of novel therapies for mCRPC, the prognostic stratification of patients undergoing 223RaCl2 has a fundamental role for clinical decision-making, ranging from treatment choice to optimal sequencing and potential associations. This large Italian multicenter study corroborated the prognostic value of several variables, emerging from ten years of clinical experience with 223RaCl2. Full article
(This article belongs to the Special Issue Diagnostic Imaging of Prostate Cancer)
►▼ Show Figures

Figure 1

11 pages, 1376 KiB  
Article
Role of Apparent Diffusion Coefficient Value and Apparent Diffusion Coefficient Ratio as Prognostic Factors for Prostate Cancer Aggressiveness
by Arvids Buss, Maija Radzina, Mara Liepa, Edgars Birkenfelds, Laura Saule, Karlis Miculis, Madara Mikelsone and Egils Vjaters
Diagnostics 2024, 14(21), 2438; https://doi.org/10.3390/diagnostics14212438 - 31 Oct 2024
Viewed by 1077
Abstract
Background: Prostate cancer is one of the most prevalent cancers in the male population. To determine the aggressiveness of suspected lesions precisely, predictive models are increasingly being developed using quantitative MRI measurements, and particularly the ADC value. This study aimed to determine whether [...] Read more.
Background: Prostate cancer is one of the most prevalent cancers in the male population. To determine the aggressiveness of suspected lesions precisely, predictive models are increasingly being developed using quantitative MRI measurements, and particularly the ADC value. This study aimed to determine whether ADC values could be used to establish the aggressiveness of prostate cancer. Methods: A retrospective single-center study included 398 patients with prostate cancer who underwent a multiparametric MRI prior to radical prostatectomy. DWI ADC values were measured (µm2/s) using b values of 50 and 1000. The dominant lesion best visualized on MRI was analyzed. The ADC values of the index lesion and reference tissue were compared to tumor aggressivity according to the Gleason grade groups based on radical prostatectomy results. Statistical analysis was performed using the Mann–Whitney U test, Kruskal–Wallis H test, Spearman’s rank correlation, and ROC curves. Results: A very strong negative correlation (rs = −0.846, p < 0.001) between ADC and GS was found. ROC analysis revealed an AUC of 0.958 and an ADC threshold value of 758 µm2/s in clinically significant prostate cancer diagnoses using the absolute ADC value, with no advantage of using the ADC ratio over the absolute ADC value being identified. Conclusion: DWI ADC values and the calculated ADC ratio have a significant inverse correlation with GS. The findings indicate a strong capability in determining prostate cancer aggressiveness, as well as the possibility of assisting with assigning PI-RADS categories using ADC as quantitative metrics. Full article
(This article belongs to the Special Issue Diagnostic Imaging of Prostate Cancer)
►▼ Show Figures

Figure 1

Other

Jump to: Research

9 pages, 1697 KiB  
Brief Report
How Standard of Truth Methodology Impacts Diagnostic PSMA-Targeting Radiopharmaceutical Evaluation: Learnings from the Phase 3 SPOTLIGHT Study
by Benjamin H. Lowentritt, Albert Chau and Phillip Davis
Diagnostics 2025, 15(4), 473; https://doi.org/10.3390/diagnostics15040473 - 14 Feb 2025
Viewed by 411
Abstract
Objectives: To explore the impact of different standard of truth (SoT) methodologies on efficacy endpoints traditionally used in clinical trials of diagnostic radiopharmaceuticals, using data from the SPOTLIGHT study (NCT04186845) in patients with recurrent prostate cancer. Methods: Data from patients with baseline [...] Read more.
Objectives: To explore the impact of different standard of truth (SoT) methodologies on efficacy endpoints traditionally used in clinical trials of diagnostic radiopharmaceuticals, using data from the SPOTLIGHT study (NCT04186845) in patients with recurrent prostate cancer. Methods: Data from patients with baseline prostate-specific antigen (PSA) ≤ 5 ng/mL, who underwent 18F-flotufolastat imaging and had data for SoT determination, were reviewed. Majority-read patient level endpoints (verified detection rate [VDR] and patient-level positive predictive value [PPV]), and region-level PPV (in the prostate/prostate bed, pelvic lymph nodes, and extrapelvic sites) according to on-study reads by three blinded readers, were stratified by the SoT methodology (histopathology; post-PET confirmatory imaging; baseline/historic conventional imaging) used by the independent Truth Panel to verify 18F-flotufolastat-avid lesions. Differences between SoT groups for each endpoint were compared using a chi-square test (statistically significant if p < 0.0167). Results: Our analysis included 297 patients (median baseline PSA = 0.8 ng/mL): 56% (n = 166) had post-PET confirmatory imaging, 26% (n = 78) had baseline/historic conventional imaging, and 18% (n = 53) had histopathological confirmation of ≥1 PET-positive lesion. For all endpoints assessed, the highest majority-read values were achieved with histopathology SoT. For histopathology versus baseline/historic conventional imaging, VDR (77%) was 3.6-fold higher (p < 0.0001), patient-level PPV (79%) was 2.2-fold higher (p < 0.0001), and region-level PPV (50%) was 3.7-fold higher in the prostate/prostate bed (p = 0.009); smaller increases were seen in majority-read PPV in the pelvic lymph nodes (77%; 1.5-fold) and other sites (75%; 1.3-fold), but these were not of statistical significance. Conclusions: These data illustrate how SoT methods can substantially impact efficacy endpoints traditionally used in clinical trials of diagnostic radiopharmaceuticals. Notably lower endpoint values are achieved with imaging SoT than with histopathology. Full article
(This article belongs to the Special Issue Diagnostic Imaging of Prostate Cancer)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-4
Back to TopTop