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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 3996

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Dr. Po-Fan Hsieh

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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
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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

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Keywords

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

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Published Papers (6 papers)

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23 pages, 11757 KiB

Open AccessArticle

Comparative Study of Cell Nuclei Segmentation Based on Computational and Handcrafted Features Using Machine Learning Algorithms

by Rashadul Islam Sumon, Md Ariful Islam Mozumdar, Salma Akter, Shah Muhammad Imtiyaj Uddin, Mohammad Hassan Ali Al-Onaizan, Reem Ibrahim Alkanhel and Mohammed Saleh Ali Muthanna

Diagnostics 2025, 15(10), 1271; https://doi.org/10.3390/diagnostics15101271 - 16 May 2025

Viewed by 239

Abstract

Background: Nuclei segmentation is the first stage of automated microscopic image analysis. The cell nucleus is a crucial aspect in segmenting to gain more insight into cell characteristics and functions that enable computer-aided pathology for early disease detection, such as prostate cancer, breast cancer, brain tumors, and other diagnoses. Nucleus segmentation remains a challenging task despite significant advancements in automated methods. Traditional techniques, such as Otsu thresholding and watershed approaches, are ineffective in challenging scenarios. However, deep learning-based methods exhibit remarkable results across various biological imaging modalities, including computational pathology. Methods: This work explores machine learning approaches for nuclei segmentation by evaluating the quality of nuclei image segmentation. We employed several methods, including K-means clustering, Random Forest (RF), Support Vector Machine (SVM) with handcrafted features, and Logistic Regression (LR) using features derived from Convolutional Neural Networks (CNNs). Handcrafted features extract attributes like the shape, texture, and intensity of nuclei and are meticulously developed based on specialized knowledge. Conversely, CNN-based features are automatically acquired representations that identify complex patterns in nuclei images. To assess how effectively these techniques segment cell nuclei, their performance is evaluated. Results: Experimental results show that Logistic Regression based on CNN-derived features outperforms the other techniques, achieving an accuracy of 96.90%, a Dice coefficient of 74.24, and a Jaccard coefficient of 55.61. In contrast, the Random Forest, Support Vector Machine, and K-means algorithms yielded lower segmentation performance metrics. Conclusions: The conclusions suggest that leveraging CNN-based features in conjunction with Logistic Regression significantly enhances the accuracy of cell nuclei segmentation in pathological images. This approach holds promise for refining computer-aided pathology workflows, potentially leading to more reliable and earlier disease diagnoses. Full article

(This article belongs to the Special Issue Diagnostic Imaging of Prostate Cancer)

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17 pages, 2449 KiB

Open AccessArticle

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 708

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 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)

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11 pages, 438 KiB

Open AccessArticle

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 Frantellizzi Show full author list Hide full author list

Diagnostics 2025, 15(3), 339; https://doi.org/10.3390/diagnostics15030339 - 31 Jan 2025

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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 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)

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11 pages, 1376 KiB

Open AccessArticle

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

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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 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)

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14 pages, 1375 KiB

Open AccessSystematic Review

Detection Rates of PSMA-PET Radiopharmaceuticals in Recurrent Prostate Cancer: A Systematic Review

by Soroush Rais-Bahrami, Phillip Davis, Albert Chau, Samuel J. Galgano, Brian F. Chapin, David M. Schuster and Catriona M. Turnbull

Diagnostics 2025, 15(10), 1224; https://doi.org/10.3390/diagnostics15101224 - 13 May 2025

Viewed by 271

Abstract

Background/Objectives: To conduct a systematic review to evaluate the detection rates (DR) of the three FDA-approved PSMA-targeted radiopharmaceuticals in patients with recurrent prostate cancer. Methods: Two individuals systematically searched MEDLINE, ScienceDirect, and Cochrane Libraries (February 2025), and independently reviewed all results to identify studies reporting patient-level ⁶⁸Ga-PSMA-11, ¹⁸F-DCFPyL, or ¹⁸F-flotufolastat DR in ≥100 evaluable patients with recurrent prostate cancer. Sample-weighted means (SWM) of extracted DR were calculated. Results: Of 5059 published articles, 37 met our inclusion criteria, reporting data from 8843 patients undergoing ⁶⁸Ga-PSMA-11 (n = 27), ¹⁸F-DCFPyL (n = 8), or ¹⁸F-flotufolastat (n = 2) studies. Heterogeneity was noted across enrolled populations, particularly in prior treatments. ⁶⁸Ga-PSMA-11 studies recruited patients with marginally higher median PSA than ¹⁸F-DCFPyL or ¹⁸F-flotufolastat studies (median PSA ranged from 0.1 to 10.7, 0.2–2.5, and 0.6–1.1, respectively). Reported overall DR ranged from 25 to 91% for ⁶⁸Ga-PSMA-11, 49–86% for ¹⁸F-DCFPyL, and 73–83% for ¹⁸F-flotufolastat, with SWM of 71%, 66%, and 79%, respectively. Post-prostatectomy DR were reported in 18 articles, resulting in SWM DR of 58% for ⁶⁸Ga-PSMA-11 (n = 12), 55% for ¹⁸F-DCFPyL (n = 4), and 76% for ¹⁸F-flotufolastat (n = 2). Among post-radiotherapy patients, SWM were 87% for ⁶⁸Ga-PSMA-11 (n = 4), 90% for ¹⁸F-DCFPyL (n = 2), and 99% for ¹⁸F-flotufolastat (n = 1). SWM DR at PSA < 1 ng/mL were 53%, 42%, and 66% for ⁶⁸Ga-PSMA-11 (n = 13), ¹⁸F-DCFPyL (n = 5), and ¹⁸F-flotufolastat (n = 2), respectively. Conclusions: Considerable heterogeneity exists across populations in studies of diagnostic PET radiopharmaceuticals. Despite a paucity of ¹⁸F-DCFPyL and ¹⁸F-flotufolastat studies compared with ⁶⁸Ga-PSMA-11, the available data suggest that all three radiopharmaceuticals provide high overall DR in patients with biochemical recurrence of prostate cancer. Full article

(This article belongs to the Special Issue Diagnostic Imaging of Prostate Cancer)

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9 pages, 1697 KiB

Open AccessBrief 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 451

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 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)

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