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Imaging-Based Diagnosis of Prostate Cancer: State of the Art

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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 2024 | Viewed by 9920

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Dr. Rulon R. Mayer

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Department of Radiation Oncology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
Interests: prostate cancer

Special Issue Information

Dear Colleagues,

Worldwide, one million men receive a prostate cancer (PCa) diagnosis and 300,000 men die from PCa. PCa, therefore, poses a significant economic and societal burden. Proper patient care and management of prostate cancer relies on the correct detection of malignant lesions and assessment of potential metastases and further growth. Non-invasive measurement of Prostate Serum Antigen (PSA) provides pre-screening and preliminary assessment for possible need for medical intervention. The widely implemented PSA indicator has significantly reduced PCa mortality, although its low specificity leads to under and overtreatment. Following PSA detection above threshold levels, prostate cancer is standardly diagnosed and risk-stratified through 6–12 core transrectal ultrasound-based needle biopsies and supplemented by MRI. However, invasive biopsies risk inflicting pain, hemorrhage, and infection for the patient. In addition, misplacement of the needle may result in underestimating the tumor score and improperly assessing the status of the patient.

To improve PCa diagnosis, grading, and alleviate patient suffering, non-invasive strategies have been developed, such as through imaging of patients with suspected disease. The entire prostate gland can be non-invasively viewed, minimizing the likelihood of missing detection of the most malignant part of a tumor. Multiparametric magnetic resonance imaging (mpMRI), fused with Ultrasound (US), and Positron Emission Tomography combined with Computed Tomography (PET/CT), is playing an increasingly important role in the early diagnosis of prostate cancer. Prostate Imaging Reporting and Data System (PI-RADS) is a semi-quantitative protocol for radiologists to visually assess multiple MRI sequences and combine them to predict the prostate tumor’s potential aggressiveness. More quantitative approaches such as applying artificial intelligence (AI) techniques to images of patients with prostate cancer. AI harnesses the available image data, and the growing computing power is popular and successful.

This Special Issue of Diagnostics, entitled “Imaging-Based Diagnosis of Prostate Cancer: State of the Art”, compiles articles on a number of research areas, such as, but not restricted to:

Scanning patients with suspected PCa with a number of imaging modalities, such as multi-parametric MRI, fused with ultrasound, Positron Emission Tomography combined with Computed Tomography (PET/CT) have been used to detect prostate cancer and localize the lesion.
Enhancement to AI applied to MP-MRI through refinements of neural algorithms, texture generation.
Combining patient data with imaging to predict clinically significant prostate cancer (csPCa).
Applying supervised and unsupervised target detection algorithms to spatially registered multi-parametric MRI to assess prostate cancer.
Spatial registration techniques.
Incorporating and combining novel biomarkers with imaging to predict clinically significant prostate cancer.
Comparison of results from different clinics, clinical situations (i.e., different magnetic fields).

Dr. Rulon R. Mayer
Guest Editor

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Keywords

prostate cancer
multi-parametric MRI (mpMRI)
positron emission tomography (PET)
computed tomography (CT)
ultrasound (US)
artificial intelligence (AI)
convolutional neural network
supervised target detection algorithms
spatially registered multi-parametric MRI

Published Papers (9 papers)

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14 pages, 2088 KiB

Open AccessArticle

Relationship between Eccentricity and Volume Determined by Spectral Algorithms Applied to Spatially Registered Bi-Parametric MRI and Prostate Tumor Aggressiveness: A Pilot Study

by Rulon Mayer, Baris Turkbey, Peter L. Choyke and Charles B. Simone II

Diagnostics 2023, 13(20), 3238; https://doi.org/10.3390/diagnostics13203238 - 17 Oct 2023

Viewed by 721

Abstract

(1) Background: Non-invasive prostate cancer assessments using multi-parametric MRI are essential to the reliable detection of lesions and proper management of patients. While current guidelines call for the administration of Gadolinium-containing intravenous contrast injections, eliminating such injections would simplify scanning and reduce patient risk and costs. However, augmented image analysis is necessary to extract important diagnostic information from MRIs. Purpose: This study aims to extend previous work on the signal to clutter ratio and test whether prostate tumor eccentricity and volume are indicators of tumor aggressiveness using bi-parametric (BP)-MRI. (2) Methods: This study retrospectively processed 42 consecutive prostate cancer patients from the PI-CAI data collection. BP-MRIs (apparent diffusion coefficient, high b-value, and T2 images) were resized, translated, cropped, and stitched to form spatially registered BP-MRIs. The International Society of Urological Pathology (ISUP) grade was used to judge cases of prostate cancer as either clinically significant prostate cancer (CsPCa) (ISUP ≥ 2) or clinically insignificant prostate cancer (CiPCa) (ISUP < 2). The Adaptive Cosine Estimator (ACE) algorithm was applied to the BP-MRIs, followed by thresholding, and then eccentricity and volume computations, from the labeled and blobbed detection maps. Then, univariate and multivariate linear regression fittings of eccentricity and volume were applied to the ISUP grade. The fits were quantitatively evaluated by computing correlation coefficients (R) and p-values. Area under the curve (AUC) and receiver operator characteristic (ROC) curve scores were used to assess the logistic fitting to CsPCa/CiPCa. (3) Results: Modest correlation coefficients (R) (>0.35) and AUC scores (0.70) for the linear and/or logistic fits from the processed prostate tumor eccentricity and volume computations for the spatially registered BP-MRIs exceeded fits using the parameters of prostate serum antigen, prostate volume, and patient age (R~0.17). (4) Conclusions: This is the first study that applied spectral approaches to BP-MRIs to generate tumor eccentricity and volume metrics to assess tumor aggressiveness. This study found significant values of R and AUC (albeit below those from multi-parametric MRI) to fit and relate the metrics to the ISUP grade and CsPCA/CiPCA, respectively. Full article

(This article belongs to the Special Issue Imaging-Based Diagnosis of Prostate Cancer: State of the Art)

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15 pages, 1809 KiB

Open AccessArticle

Machine Learning CT-Based Automatic Nodal Segmentation and PET Semi-Quantification of Intraoperative ⁶⁸Ga-PSMA-11 PET/CT Images in High-Risk Prostate Cancer: A Pilot Study

by Guido Rovera, Serena Grimaldi, Marco Oderda, Monica Finessi, Valentina Giannini, Roberto Passera, Paolo Gontero and Désirée Deandreis

Diagnostics 2023, 13(18), 3013; https://doi.org/10.3390/diagnostics13183013 - 21 Sep 2023

Cited by 1 | Viewed by 963

Abstract

High-resolution intraoperative PET/CT specimen imaging, coupled with prostate-specific membrane antigen (PSMA) molecular targeting, holds great potential for the rapid ex vivo identification of disease localizations in high-risk prostate cancer patients undergoing surgery. However, the accurate analysis of radiotracer uptake would require time-consuming manual volumetric segmentation of 3D images. The aim of this study was to test the feasibility of using machine learning to perform automatic nodal segmentation of intraoperative ⁶⁸Ga-PSMA-11 PET/CT specimen images. Six (n = 6) lymph-nodal specimens were imaged in the operating room after an e.v. injection of 2.1 MBq/kg of ⁶⁸Ga-PSMA-11. A machine learning-based approach for automatic lymph-nodal segmentation was developed using only open-source Python libraries (Scikit-learn, SciPy, Scikit-image). The implementation of a k-means clustering algorithm (n = 3 clusters) allowed to identify lymph-nodal structures by leveraging differences in tissue density. Refinement of the segmentation masks was performed using morphological operations and 2D/3D-features filtering. Compared to manual segmentation (ITK-SNAP v4.0.1), the automatic segmentation model showed promising results in terms of weighted average precision (97–99%), recall (68–81%), Dice coefficient (80–88%) and Jaccard index (67–79%). Finally, the ML-based segmentation masks allowed to automatically compute semi-quantitative PET metrics (i.e., SUVmax), thus holding promise for facilitating the semi-quantitative analysis of PET/CT images in the operating room. Full article

(This article belongs to the Special Issue Imaging-Based Diagnosis of Prostate Cancer: State of the Art)

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13 pages, 1836 KiB

Open AccessArticle

MRI-Based Surrogate Imaging Markers of Aggressiveness in Prostate Cancer: Development of a Machine Learning Model Based on Radiomic Features

by Ignacio Dominguez, Odette Rios-Ibacache, Paola Caprile, Jose Gonzalez, Ignacio F. San Francisco and Cecilia Besa

Diagnostics 2023, 13(17), 2779; https://doi.org/10.3390/diagnostics13172779 - 28 Aug 2023

Cited by 3 | Viewed by 1085

Abstract

This study aimed to develop a noninvasive Machine Learning (ML) model to identify clinically significant prostate cancer (csPCa) according to Gleason Score (GS) based on biparametric MRI (bpMRI) radiomic features and clinical information. Methods: This retrospective study included 86 adult Hispanic men (60 ± 8.2 years, median prostate-specific antigen density (PSA-D) 0.15 ng/mL²) with PCa who underwent prebiopsy 3T MRI followed by targeted MRI–ultrasound fusion and systematic biopsy. Two observers performed 2D segmentation of lesions in T2WI/ADC images. We classified csPCa (GS ≥ 7) vs. non-csPCa (GS = 6). Univariate statistical tests were performed for different parameters, including prostate volume (PV), PSA-D, PI-RADS, and radiomic features. Multivariate models were built using the automatic feature selection algorithm Recursive Feature Elimination (RFE) and different classifiers. A stratified split separated the train/test (80%) and validation (20%) sets. Results: Radiomic features derived from T2WI/ADC are associated with GS in patients with PCa. The best model found was multivariate, including image (T2WI/ADC) and clinical (PV and PSA-D) information. The validation area under the curve (AUC) was 0.80 for differentiating csPCa from non-csPCa, exhibiting better performance than PI-RADS (AUC: 0.71) and PSA-D (AUC: 0.78). Conclusion: Our multivariate ML model outperforms PI-RADS v2.1 and established clinical indicators like PSA-D in classifying csPCa accurately. This underscores MRI-derived radiomics’ (T2WI/ADC) potential as a robust biomarker for assessing PCa aggressiveness in Hispanic patients. Full article

(This article belongs to the Special Issue Imaging-Based Diagnosis of Prostate Cancer: State of the Art)

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10 pages, 696 KiB

Open AccessArticle

The Impact of Prostate Volume on the Prostate Imaging and Reporting Data System (PI-RADS) in a Real-World Setting

by Yannic Volz, Maria Apfelbeck, Nikolaos Pyrgidis, Paulo L. Pfitzinger, Elena Berg, Benedikt Ebner, Benazir Enzinger, Troya Ivanova, Michael Atzler, Philipp M. Kazmierczak, Dirk-André Clevert, Christian Stief and Michael Chaloupka

Diagnostics 2023, 13(16), 2677; https://doi.org/10.3390/diagnostics13162677 - 15 Aug 2023

Cited by 2 | Viewed by 880

Abstract

Multiparametric magnetic resonance imaging (mpMRI) has emerged as a new cornerstone in the diagnostic pathway of prostate cancer. However, mpMRI is not devoid of factors influencing its detection rate of clinically significant prostate cancer (csPCa). Amongst others, prostate volume has been demonstrated to influence the detection rates of csPCa. Particularly, increasing volume has been linked to a reduced cancer detection rate. However, information about the linkage between PI-RADS, prostate volume and detection rate is relatively sparse. Therefore, the current study aims to assess the association between prostate volume, PI-RADS score and detection rate of csP-Ca, representing daily practice and contemporary mpMRI expertise. Thus, 1039 consecutive patients with 1151 PI-RADS targets, who underwent mpMRI-guided prostate biopsy at our tertiary referral center, were included. Prior mpMRI had been assessed by a plethora of 111 radiology offices, including academic centers and private practices. mpMRI was not secondarily reviewed in house before biopsy. mpMRI-targeted biopsy was performed by a small group of a total of ten urologists, who had performed at least 100 previous biopsies. Using ROC analysis, we defined cut-off values of prostate volume for each PI-RADS score, where the detection rate drops significantly. For PI-RADS 4 lesions, we found a volume > 61.5 ccm significantly reduced the cancer detection rate (OR 0.24; 95% CI 0.16–0.38; p < 0.001). For PI-RADS 5 lesions, we found a volume > 51.5 ccm to significantly reduce the cancer detection rate (OR 0.39; 95% CI 0.25–0.62; p < 0.001). For PI-RADS 3 lesions, none of the evaluated clinical parameters had a significant impact on the detection rate of csPCa. In conclusion, we show that enlarged prostate volume represents a major limitation in the daily practice of mpMRI-targeted biopsy. This study is the first to define exact cut-off values of prostate volume to significantly impair the validity of PI-RADS assessed in a real-world setting. Therefore, the results of mpMRI-targeted biopsy should be interpreted carefully, especially in patients with prostate volumes above our defined thresholds. Full article

(This article belongs to the Special Issue Imaging-Based Diagnosis of Prostate Cancer: State of the Art)

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13 pages, 1781 KiB

Open AccessArticle

Comparison of the Effects of DOTA and NOTA Chelators on ⁶⁴Cu-Cudotadipep and ⁶⁴Cu-Cunotadipep for Prostate Cancer

by Inki Lee, Min Hwan Kim, Kyongkyu Lee, Keumrok Oh, Hyunwoo Lim, Jae Hun Ahn, Yong Jin Lee, Gi Jeong Cheon, Dae Yoon Chi and Sang Moo Lim

Diagnostics 2023, 13(16), 2649; https://doi.org/10.3390/diagnostics13162649 - 11 Aug 2023

Viewed by 1680

Abstract

Background: This study compared the effects of 1,4,7,10-tetraazacyclododecane-1,4,7,10-tetraacetic acid (DOTA) and 1,4,7-triazacyclononane-1,4,7-triacetic acid (NOTA) as ⁶⁴Cu-chelating agents in newly developed prostate-specific membrane antigen (PSMA) target compounds, ⁶⁴Cu-cudotadipep and ⁶⁴Cu-cunotadipep, on pharmacokinetics. Methods: The in vitro stability of the chelators was evaluated using human and mouse serum. In vitro PSMA-binding affinity and cell uptake were compared using human 22Rv1 cells. To evaluate specific PSMA-expressing tumor-targeting efficiency, micro-positron emission tomography (mcroPET)/computed tomography (CT) and biodistribution analysis were performed using PSMA+ PC3-PIP and PSMA− PC3-flu tumor xenografts. Results: The serum stability of DOTA- or NOTA-conjugated ⁶⁴Cu-cudotadipep and ⁶⁴Cu-cunotadipep was >97%. The Ki value of the NOTA derivative, cunotadipep, in the in vitro affinity binding analysis was higher (2.17 ± 0.25 nM) than that of the DOTA derivative, cudotadipep (6.75 ± 0.42 nM). The cunotadipep exhibited a higher cellular uptake (6.02 ± 0.05%/1 × 10⁶ cells) compared with the cudotadipep (2.93 ± 0.06%/1 × 10⁶ cells). In the biodistribution analysis and microPET/CT imaging, the ⁶⁴Cu-labeled NOTA derivative, ⁶⁴Cu-cunotadipep, demonstrated a greater tumor uptake and lower liver uptake than the DOTA derivative. Conclusions: This study indicates that the PSMA-targeted ⁶⁴Cu-cunotadipep can be applied in clinical practice owing to its high diagnostic power for prostate cancer. Full article

(This article belongs to the Special Issue Imaging-Based Diagnosis of Prostate Cancer: State of the Art)

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11 pages, 1398 KiB

Open AccessArticle

Perilesional Targeted Biopsy Combined with MRI-TRUS Image Fusion-Guided Targeted Prostate Biopsy: An Analysis According to PI-RADS Scores

by Masayuki Tomioka, Kensaku Seike, Hiromi Uno, Nami Asano, Haruo Watanabe, Risa Tomioka-Inagawa, Makoto Kawase, Daiki Kato, Manabu Takai, Koji Iinuma, Yuki Tobisawa, Keita Nakane, Kunihiro Tsuchiya, Takayasu Ito and Takuya Koie

Diagnostics 2023, 13(15), 2608; https://doi.org/10.3390/diagnostics13152608 - 07 Aug 2023

Viewed by 880

Abstract

A prostate-targeted biopsy (TB) core is usually collected from a site where magnetic resonance imaging (MRI) indicates possible cancer. However, the extent of the lesion is difficult to accurately predict using MRI or TB alone. Therefore, we performed several biopsies around the TB site (perilesional [p] TB) and analyzed the association between the positive cores obtained using TB and pTB and the Prostate Imaging Reporting and Data System (PI-RADS) scores. This retrospective study included patients who underwent prostate biopsies. The extent of pTB was defined as the area within 10 mm of a TB site. A total of 162 eligible patients were enrolled. Prostate cancer (PCa) was diagnosed in 75.2% of patients undergoing TB, with a positivity rate of 50.7% for a PI-RADS score of 3, 95.8% for a PI-RADS score of 4, and 100% for a PI-RADS score of 5. Patients diagnosed with PCa according to both TB and pTB had significantly higher positivity rates for PI-RADS scores of 4 and 5 than for a PI-RADS score of 3 (p < 0.0001 and p = 0.0009, respectively). Additional pTB may be performed in patients with PI-RADS ≥ 4 regions of interest for assessing PCa malignancy. Full article

(This article belongs to the Special Issue Imaging-Based Diagnosis of Prostate Cancer: State of the Art)

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13 pages, 1682 KiB

Open AccessArticle

Application of Spectral Algorithm Applied to Spatially Registered Bi-Parametric MRI to Predict Prostate Tumor Aggressiveness: A Pilot Study

by Rulon Mayer, Baris Turkbey, Peter L. Choyke and Charles B. Simone II

Diagnostics 2023, 13(12), 2008; https://doi.org/10.3390/diagnostics13122008 - 09 Jun 2023

Cited by 1 | Viewed by 964

Abstract

Background: Current prostate cancer evaluation can be inaccurate and burdensome. Quantitative evaluation of Magnetic Resonance Imaging (MRI) sequences non-invasively helps prostate tumor assessment. However, including Dynamic Contrast Enhancement (DCE) in the examined MRI sequence set can add complications, inducing possible side effects from the IV placement or injected contrast material and prolonging scanning time. More accurate quantitative MRI without DCE and artificial intelligence approaches are needed. Purpose: Predict the risk of developing Clinically Significant (Insignificant) prostate cancer CsPCa (CiPCa) and correlate with the International Society of Urologic Pathology (ISUP) grade using processed Signal to Clutter Ratio (SCR) derived from spatially registered bi-parametric MRI (SRBP-MRI) and thereby enhance non-invasive management of prostate cancer. Methods: This pilot study retrospectively analyzed 42 consecutive prostate cancer patients from the PI-CAI data collection. BP-MRI (Apparent Diffusion Coefficient, High B-value, T2) were resized, translated, cropped, and stitched to form spatially registered SRBP-MRI. Efficacy of noise reduction was tested by regularizing, eliminating principal components (PC), and minimizing elliptical volume from the covariance matrix to optimize the SCR. MRI guided biopsy (MRBx), Systematic Biopsy (SysBx), combination (MRBx + SysBx), or radical prostatectomy determined the ISUP grade for each patient. ISUP grade ≥ 2 (<2) was judged as CsPCa (CiPCa). Linear and logistic regression were fitted to ISUP grade and CsPCa/CiPCa SCR. Correlation Coefficients (R) and Area Under the Curves (AUC) for Receiver Operator Curves (ROC) evaluated the performance. Results: High correlation coefficients (R) (>0.55) and high AUC (=1.0) for linear and/or logistic fit from processed SCR and z-score for SRBP-MRI greatly exceed fits using prostate serum antigen, prostate volume, and patient age (R ~ 0.17). Patients assessed with combined MRBx + SysBx and from individual MRI scanners achieved higher R (DR = 0.207+/−0.118) than all patients used in the fits. Conclusions: In the first study, to date, spectral approaches for assessing tumor aggressiveness on SRBP-MRI have been applied and tested and achieved high values of R and exceptional AUC to fit the ISUP grade and CsPCA/CiPCA, respectively. Full article

(This article belongs to the Special Issue Imaging-Based Diagnosis of Prostate Cancer: State of the Art)

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11 pages, 524 KiB

Open AccessArticle

Reliability of Multiparametric Magnetic Resonance Imaging in Patients with a Previous Negative Biopsy: Comparison with Biopsy-Naïve Patients in the Detection of Clinically Significant Prostate Cancer

by Biagio Barone, Luigi Napolitano, Francesco Paolo Calace, Dario Del Biondo, Giorgio Napodano, Marco Grillo, Pasquale Reccia, Luigi De Luca, Domenico Prezioso, Matteo Muto, Felice Crocetto and Matteo Ferro

Diagnostics 2023, 13(11), 1939; https://doi.org/10.3390/diagnostics13111939 - 01 Jun 2023

Cited by 4 | Viewed by 1103

Abstract

Background: Multiparametric magnetic resonance is an established imaging utilized in the diagnostic pathway of prostate cancer. The aim of this study is to evaluate the accuracy and reliability of multiparametric magnetic resonance imaging (mpMRI) in the detection of clinically significant prostate cancer, defined as Gleason Score ≥ 4 + 3 or a maximum cancer core length 6 mm or longer, in patients with a previous negative biopsy. Methods: The study was conducted as a retrospective observational study at the University of Naples “Federico II”, Italy. Overall, 389 patients who underwent systematic and target prostate biopsy between January 2019 and July 2020 were involved and were divided into two groups: Group A, which included biopsy-naïve patients; Group B, which included re-biopsy patients. All mpMRI images were obtained using three Tesla instruments and were interpreted according to PIRADS (Prostate Imaging Reporting and Data System) version 2.0. Results: 327 patients were biopsy-naïve, while 62 belonged to the re-biopsy group. Both groups were comparable in terms of age, total PSA (prostate-specific antigen), and number of cores obtained at the biopsy. 2.2%, 8.8%, 36.1%, and 83.4% of, respectively, PIRADS 2, 3, 4, and 5 biopsy-naïve patients reported a clinically significant prostate cancer compared to 0%, 14.3%, 39%, and 66.6% of re-biopsy patients (p < 0.0001–p = 0.040). No difference was reported in terms of post-biopsy complications. Conclusions: mpMRI confirms its role as a reliable diagnostic tool prior to performing prostate biopsy in patients who underwent a previous negative biopsy, reporting a comparable detection rate of clinically significant prostate cancer. Full article

(This article belongs to the Special Issue Imaging-Based Diagnosis of Prostate Cancer: State of the Art)

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5 pages, 886 KiB

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Cardiac Metastasis from Prostate Cancer: A Case Study Underlying the Crucial Role of the PSMA PET/CT

by Annalice Gandini, Matteo Bauckneht, Luca Sofia, Laura Tomasello, Giuseppe Fornarini and Elisa Zanardi

Diagnostics 2023, 13(17), 2733; https://doi.org/10.3390/diagnostics13172733 - 23 Aug 2023

Viewed by 866

Abstract

Prostate cancer still represents one of the most frequent cancers and causes of death worldwide, despite the huge therapeutic advances in the last decades. The introduction into clinical practice of prostate-specific membrane antigen positron emission tomography/computed tomography (PSMA PET/CT) has significantly improved diagnostic capacity, allowing for the identification of lesions previously undetectable. The case we are presenting is about a 90-year-old man affected by metastatic prostate cancer and treated with hormonal therapies. At the second progression, the restaging with PSMA PET/CT pointed out a millimetric cardiac intra-atrial metastasis, on which little/scarce literature data are still available. On one hand, this finding confirms the high sensitivity of this technique, which should be preferred over traditional imaging. On the other hand, it suggests that introducing next-generation imaging into clinical practice may provide novel insights about prostate cancer metastatic spread. Full article

(This article belongs to the Special Issue Imaging-Based Diagnosis of Prostate Cancer: State of the Art)

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