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Non-invasive Methods for Screening, Prognosis, and Prediction in Prostate Cancers

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A special issue of Cancers (ISSN 2072-6694). This special issue belongs to the section "Cancer Biomarkers".

Deadline for manuscript submissions: closed (30 June 2023) | Viewed by 24985

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Special Issue Editors

Dr. Yoganand Balagurunathan

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Guest Editor

Department of Machine Learning, H. Lee. Moffitt Cancer Center, Tampa, FL 33612, USA
Interests: machine learning; medical image processing; multimodal data fusion; prostate cancer; lung cancer; lylmphoma
Special Issues, Collections and Topics in MDPI journals

Dr. Kenneth Gage

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Guest Editor

Department of Diagnostic Radiology, H.L. Moffitt Cancer Center, Tampa, FL 33612, USA
Interests: use of MRI and functional imaging for the detection and classification of prostate cancers; post-treatment complications; differentiation of malignant from premalignant and nonmalignant diseases

Prof. Dr. Lubomir Hadjiiski

E-Mail Website
Guest Editor

Department of Radiology, University of Michigan Medical School, Ann Arbor, MI 48109, USA
Interests: machine learning/artificial intelligence; predictive models; image processing; medical imaging; design of decision support systems for detection and diagnosis of cancer in different organs; quantitative analysis of image biomarkers for treatment response monitoring

Special Issue Information

Dear Colleagues,

We are delighted to extend an invitation to contribute to this Special issue titled ‘Non-invasive Methods for Screening, Prognosis and Prediction in Prostate cancers’, part of MDPI journal Cancers (ISSN 2072-6694, details below), with an 5-year impact factor of 6.433 (2019).

The last two decades have seen tremendous progress in our understanding of cancer biology with the advent of sequencing of the human genome, which led to a multifaceted investigatory effort to study the physiology and epidemiological aspects of the disease, and finer genome investigation, as well as the study of the proteome and the control of drug interactions. All these approaches have contributed tremendously to our understanding and management of cancer and have been further enhanced with the recent development of big data machine learning methods and artificial intelligence (AI) techniques.

Imaging has played a larger role in oncology both in terms of diagnosing a disease condition and detecting disease progression. In prostate cancers, disease detection and treatments have made strides with the advent of the transrectal (recently transperineal) ultrasound biopsy system fused with mpMRI, which has allowed advancements in localized therapies with the use of U/S-based technologies. Genomic sequencing has led to finer mutational analysis of disease grades and identifying novel gene fusions (such as TMPRSS-ERG) that lead to progressive disease in some cases. The use of advanced imaging methods both in radiology and pathology, coupled with machine learning approaches (AI methods), has helped us to develop quantitative methods in imaging. Biofluid research based on a patient’s blood and urine has evolved to provide alternative biomarkers to assess underlying disease pathology.

In this Special Issue, we would like to present an ensemble of methods in prostate cancer disease detection, diagnosis, progression prediction, prognosis, and management, including epidemiological studies. We encourage submissions from multidisciplinary area, including original research methods, review articles, and opinion summaries to this Special Issue. We will consider related research findings, methods or approaches that have relevance in prostate oncology.

The areas of interest of this Special Issue include but are not limited to:

Prostate cancer disease detection, diagnosis, and prognosis;
Epidemiological studies in disease detection or progression;
Imaging methods in disease detection (mpMRI, CT, PET);
Targeted imaging methods in disease detection (e.g., PSMA-PET);
Targeted treatment techniques;
Advanced machine learning and artificial-intelligence-based imaging methods;
Biofluid-based methods in disease prediction or prognosis;
Genomic markers for disease management;
Fusion of multimodality biomarkers for disease prediction, diagnosis, or prognosis.

Dr. Yoganand Balagurunathan
Dr. Kenneth Gage
Prof. Dr. Lubomir Hadjiiski
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. Cancers 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 2900 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

screening, prognosis, and prediction of prostate cancer
biology and staging of prostate disease
non-invasive biomarkers—clinical, imaging, fluids (blood, urine), etc
computed tomography (CT), ultrasound (US) or multiparametric magnetic resonance imaging (mpMRI)-based biomarker
clinical trials and innovations in patient care
focal or cryotherapy in prostate cancer
artificial Intelligence and Learning methods in prostate oncology

Published Papers (14 papers)

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Research

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11 pages, 2547 KiB

Open AccessArticle

Quantitative Evaluation of Apparent Diffusion Coefficient Values, ISUP Grades and Prostate-Specific Antigen Density Values of Potentially Malignant PI-RADS Lesions

by Nadine Spadarotto, Anja Sauck, Nicolin Hainc, Isabelle Keller, Hubert John and Joachim Hohmann

Cancers 2023, 15(21), 5183; https://doi.org/10.3390/cancers15215183 - 28 Oct 2023

Viewed by 670

Abstract

The aim of this study was to demonstrate the correlation between ADC values and the ADC/PSAD ratio for potentially malignant prostate lesions classified into ISUP grades and to determine threshold values to differentiate benign lesions (noPCa), clinically insignificant (nsPCa) and clinically significant prostate cancer (csPCa). We enrolled a total of 403 patients with 468 prostate lesions, of which 46 patients with 50 lesions were excluded for different reasons. Therefore, 357 patients with a total of 418 prostate lesions remained for the final evaluation. For all lesions, ADC values were measured; they demonstrated a negative correlation with ISUP grades (p < 0.001), with a significant difference between csPCa and a combined group of nsPCa and noPCa (ns-noPCa, p < 0.001). The same was true for the ADC/PSAD ratio, but only the ADC/PSAD ratio proved to be a significant discriminator between nsPCa and noPCa (p = 0.0051). Using the calculated threshold values, up to 31.6% of biopsies could have been avoided. Furthermore, the ADC/PSAD ratio, with the ability to distinguish between nsPCa and noPCa, offers possible active surveillance without prior biopsy. Full article

(This article belongs to the Special Issue Non-invasive Methods for Screening, Prognosis, and Prediction in Prostate Cancers)

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13 pages, 2003 KiB

Open AccessArticle

T2-Weighted MRI Radiomic Features Predict Prostate Cancer Presence and Eventual Biochemical Recurrence

by Savannah R. Duenweg, Samuel A. Bobholz, Michael J. Barrett, Allison K. Lowman, Aleksandra Winiarz, Biprojit Nath, Margaret Stebbins, John Bukowy, Kenneth A. Iczkowski, Kenneth M. Jacobsohn, Stephanie Vincent-Sheldon and Peter S. LaViolette

Cancers 2023, 15(18), 4437; https://doi.org/10.3390/cancers15184437 - 06 Sep 2023

Viewed by 999

Abstract

Prostate cancer (PCa) is the most diagnosed non-cutaneous cancer in men. Despite therapies such as radical prostatectomy, which is considered curative, distant metastases may form, resulting in biochemical recurrence (BCR). This study used radiomic features calculated from multi-parametric magnetic resonance imaging (MP-MRI) to evaluate their ability to predict BCR and PCa presence. Data from a total of 279 patients, of which 46 experienced BCR, undergoing MP-MRI prior to surgery were assessed for this study. After surgery, the prostate was sectioned using patient-specific 3D-printed slicing jigs modeled using the T2-weighted imaging (T2WI). Sectioned tissue was stained, digitized, and annotated by a GU-fellowship trained pathologist for cancer presence. Digitized slides and annotations were co-registered to the T2WI and radiomic features were calculated across the whole prostate and cancerous lesions. A tree regression model was fitted to assess the ability of radiomic features to predict BCR, and a tree classification model was fitted with the same radiomic features to classify regions of cancer. We found that 10 radiomic features predicted eventual BCR with an AUC of 0.97 and classified cancer at an accuracy of 89.9%. This study showcases the application of a radiomic feature-based tool to screen for the presence of prostate cancer and assess patient prognosis, as determined by biochemical recurrence. Full article

(This article belongs to the Special Issue Non-invasive Methods for Screening, Prognosis, and Prediction in Prostate Cancers)

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12 pages, 871 KiB

Open AccessArticle

Circulating Tumor DNA Analysis on Metastatic Prostate Cancer with Disease Progression

by Sungun Bang, Dongju Won, Saeam Shin, Kang Su Cho, Jae Won Park, Jongsoo Lee, Young Deuk Choi, Suwan Kang, Seung-Tae Lee, Jong Rak Choi and Hyunho Han

Cancers 2023, 15(15), 3998; https://doi.org/10.3390/cancers15153998 - 07 Aug 2023

Viewed by 1292

Abstract

The positivity rate of circulating tumor DNA (ctDNA) next-generation sequencing (NGS) varies among patients with metastatic prostate cancer (mPC), complicating its incorporation into regular practice. This retrospective study analyzed the ctDNA sequencing results of 100 mPC patients from May 2021 to March 2023 to identify the factors associated with positive ctDNA. Three custom gene panels were used for sequencing. Overall, 63% of the patients exhibited tier I/II somatic alterations, while 12% had pathogenic/likely pathogenic germline alterations. The key genes that were altered included AR, TP53, RB1, PTEN, and APC. Mutations in BRCA1/2, either germline or somatic, were observed in 21% of the patients. Among the metastatic castration-resistant prostate cancer (mCRPC) patients, the ctDNA-positive samples generally showed higher median prostate-specific antigen (PSA) levels and were more likely to be at the radiographic and clinical progressive disease stages, although they were not significantly associated with PSA progression. Our results suggest that ctDNA analysis could detect meaningful genetic changes in mPC patients, especially during disease progression. Full article

(This article belongs to the Special Issue Non-invasive Methods for Screening, Prognosis, and Prediction in Prostate Cancers)

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13 pages, 962 KiB

Open AccessArticle

Usefulness of BRCA and ctDNA as Prostate Cancer Biomarkers: A Meta-Analysis

by Kinga Domrazek, Karol Pawłowski and Piotr Jurka

Cancers 2023, 15(13), 3452; https://doi.org/10.3390/cancers15133452 - 30 Jun 2023

Viewed by 1355

Abstract

Prostate cancer represents the most common male urologic neoplasia. Tissue biopsies are the gold standard in oncology for diagnosing prostate cancer. We conducted a study to find the most reliable and noninvasive diagnostic tool. We performed a systematic review and meta-analysis of two biomarkers which we believe are the most interesting: BRCA (BRCA1 and 2) and ctDNA. Our systematic research yielded 248 articles. Forty-five duplicates were first excluded and, upon further examination, a further 203 articles were excluded on the basis of the inclusion and exclusion criteria, leaving 25 articles. A statistical analysis of the obtained data has been performed. With a collective calculation, BRCA1 was expressed in 2.74% of all cases from 24,212 patients examined and BRCA2 in 1.96% of cases from 20,480 patients. In a total calculation using ctDNA, it was observed that 89% of cases from 1198 patients exhibited high expression of circulating tumor DNA. To date, no ideal PCa biomarker has been found. Although BRCA1 and BRCA2 work well for breast and ovarian cancers, they do not seem to be reliable for prostate cancer. ctDNA seems to be a much better biomarker; however, there are few studies in this area. Further studies need to be performed. Full article

(This article belongs to the Special Issue Non-invasive Methods for Screening, Prognosis, and Prediction in Prostate Cancers)

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19 pages, 3341 KiB

Open AccessArticle

Classifying Malignancy in Prostate Glandular Structures from Biopsy Scans with Deep Learning

by Ryan Fogarty, Dmitry Goldgof, Lawrence Hall, Alex Lopez, Joseph Johnson, Manoj Gadara, Radka Stoyanova, Sanoj Punnen, Alan Pollack, Julio Pow-Sang and Yoganand Balagurunathan

Cancers 2023, 15(8), 2335; https://doi.org/10.3390/cancers15082335 - 17 Apr 2023

Viewed by 1626

Abstract

Histopathological classification in prostate cancer remains a challenge with high dependence on the expert practitioner. We develop a deep learning (DL) model to identify the most prominent Gleason pattern in a highly curated data cohort and validate it on an independent dataset. The histology images are partitioned in tiles (14,509) and are curated by an expert to identify individual glandular structures with assigned primary Gleason pattern grades. We use transfer learning and fine-tuning approaches to compare several deep neural network architectures that are trained on a corpus of camera images (ImageNet) and tuned with histology examples to be context appropriate for histopathological discrimination with small samples. In our study, the best DL network is able to discriminate cancer grade (GS3/4) from benign with an accuracy of 91%, F₁-score of 0.91 and AUC 0.96 in a baseline test (52 patients), while the cancer grade discrimination of the GS3 from GS4 had an accuracy of 68% and AUC of 0.71 (40 patients). Full article

(This article belongs to the Special Issue Non-invasive Methods for Screening, Prognosis, and Prediction in Prostate Cancers)

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13 pages, 2040 KiB

Open AccessArticle

Urinary Zinc Loss Identifies Prostate Cancer Patients

by Maria Grazia Maddalone, Marco Oderda, Giulio Mengozzi, Iacopo Gesmundo, Francesco Novelli, Mirella Giovarelli, Paolo Gontero and Sergio Occhipinti

Cancers 2022, 14(21), 5316; https://doi.org/10.3390/cancers14215316 - 28 Oct 2022

Cited by 5 | Viewed by 1941

Abstract

Prostate Cancer (PCa) is one of the most common malignancies in men worldwide, with 1.4 million diagnoses and 310,000 deaths in 2020. Currently, there is an intense debate regarding the serum prostatic specific antigen (PSA) test as a diagnostic tool in PCa due to the lack of specificity and high prevalence of over-diagnosis and over-treatments. One of the most consistent characteristics of PCa is the marked decrease in zinc; hence the lost ability to accumulate and secrete zinc represents a potential parameter for early detection of the disease. We quantified zinc levels in urine samples collected after a standardized prostatic massage from 633 male subjects that received an indication for prostate biopsy from 2015 and 2019 at AOU Città della Salute e della Scienza di Torino Hospital. We observed that the mean zinc levels were lower in the urine of cancer patients than in healthy subjects, with a decreasing trend in correlation with the progression of the disease. The combination of zinc with standard parameters, such as PSA, age, digital rectal exploration results, and magnetic resonance findings, displayed high diagnostic performance. These results suggest that urinary zinc may represent an early and non-invasive diagnostic biomarker for prostate cancer. Full article

(This article belongs to the Special Issue Non-invasive Methods for Screening, Prognosis, and Prediction in Prostate Cancers)

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19 pages, 3614 KiB

Open AccessArticle

Longitudinal Changes and Predictive Value of Multiparametric MRI Features for Prostate Cancer Patients Treated with MRI-Guided Lattice Extreme Ablative Dose (LEAD) Boost Radiotherapy

by Ahmad Algohary, Mohammad Alhusseini, Adrian L. Breto, Deukwoo Kwon, Isaac R. Xu, Sandra M. Gaston, Patricia Castillo, Sanoj Punnen, Benjamin Spieler, Matthew C. Abramowitz, Alan Dal Pra, Oleksandr N. Kryvenko, Alan Pollack and Radka Stoyanova

Cancers 2022, 14(18), 4475; https://doi.org/10.3390/cancers14184475 - 15 Sep 2022

Cited by 3 | Viewed by 1673

Abstract

We investigated the longitudinal changes in multiparametric MRI (mpMRI) (T2-weighted, Apparent Diffusion Coefficient (ADC), and Dynamic Contrast Enhanced (DCE-)MRI) of prostate cancer patients receiving Lattice Extreme Ablative Dose (LEAD) radiotherapy (RT) and the capability of their imaging features to predict RT outcome based on endpoint biopsies. Ninety-five mpMRI exams from 25 patients, acquired pre-RT and at 3-, 9-, and 24-months post-RT were analyzed. MRI/Ultrasound-fused biopsies were acquired pre- and at two-years post-RT (endpoint). Five regions of interest (ROIs) were analyzed: Gross tumor volume (GTV), normally-appearing tissue (NAT) and peritumoral volume in both peripheral (PZ) and transition (TZ) zones. Diffusion and perfusion radiomics features were extracted from mpMRI and compared before and after RT using two-tailed Student t-tests. Selected features at the four scan points and their differences (Δ radiomics) were used in multivariate logistic regression models to predict the endpoint biopsy positivity. Baseline ADC values were significantly different between GTV, NAT-PZ, and NAT-TZ (p-values < 0.005). Pharmaco-kinetic features changed significantly in the GTV at 3-month post-RT compared to baseline. Several radiomics features at baseline and three-months post-RT were significantly associated with endpoint biopsy positivity and were used to build models with high predictive power of this endpoint (AUC = 0.98 and 0.89, respectively). Our study characterized the RT-induced changes in perfusion and diffusion. Quantitative imaging features from mpMRI show promise as being predictive of endpoint biopsy positivity. Full article

(This article belongs to the Special Issue Non-invasive Methods for Screening, Prognosis, and Prediction in Prostate Cancers)

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13 pages, 1228 KiB

Open AccessArticle

Reduced DNA Repair Capacity in Prostate Cancer Patients: A Phenotypic Approach Using the CometChip

by Carmen Ortiz-Sánchez, Jarline Encarnación-Medina, Jong Y. Park, Natasha Moreno, Gilberto Ruiz-Deya and Jaime Matta

Cancers 2022, 14(13), 3117; https://doi.org/10.3390/cancers14133117 - 25 Jun 2022

Cited by 2 | Viewed by 1653

Abstract

Prostate cancer (PCa) accounts for 22% of the new cases diagnosed in Hispanic men in the US. Among Hispanics, Puerto Rican (PR) men show the highest PCa-specific mortality. Epidemiological studies using functional assays in lymphocytes have demonstrated that having low DRC is a significant risk factor for cancer development. The aim of this study was to evaluate variations in DRC in PR men with PCa. Lymphocytes were isolated from blood samples from PCa cases (n = 41) and controls (n = 14) recruited at a hospital setting. DRC levels through the nucleotide excision repair (NER) pathway were measured with the CometChip using UVC as a NER inductor. The mean DRC for controls and PCa cases were 20.66% (±7.96) and 8.41 (±4.88), respectively (p < 0.001). The relationship between DRC and tumor aggressiveness was also evaluated. Additional comparisons were performed to evaluate the contributions of age, anthropometric measurements, and prostate-specific antigen levels to the DRC. This is the first study to apply the CometChip in a clinical cancer study. Our results represent an innovative step in the development of a blood-based screening test for PCa based on DRC levels. Our data also suggest that DRC levels may have the potential to discriminate between aggressive and indolent cases. Full article

(This article belongs to the Special Issue Non-invasive Methods for Screening, Prognosis, and Prediction in Prostate Cancers)

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10 pages, 691 KiB

Open AccessArticle

Volatilome Analysis in Prostate Cancer by Electronic Nose: A Pilot Monocentric Study

by Alessio Filianoti, Manuela Costantini, Alfredo Maria Bove, Umberto Anceschi, Aldo Brassetti, Mariaconsiglia Ferriero, Riccardo Mastroianni, Leonardo Misuraca, Gabriele Tuderti, Gennaro Ciliberto and Giuseppe Simone

Cancers 2022, 14(12), 2927; https://doi.org/10.3390/cancers14122927 - 14 Jun 2022

Cited by 13 | Viewed by 1617

Abstract

Urine analysis via an electronic nose provides volatile organic compounds easily usable in the diagnosis of urological diseases. Although challenging and highly expensive for health systems worldwide, no useful markers are available in clinical practice that aim to anticipate prostate cancer (PCa) diagnosis in the early stages in the context of wide population screening. Some previous works suggested that dogs trained to smell urine could recognize several types of cancers with various success rates. We hypothesized that urinary volatilome profiling may distinguish PCa patients from healthy controls. In this study, 272 individuals, 133 patients, and 139 healthy controls participated. Urine samples were collected, stabilized at 37 °C, and analyzed using a commercially available electronic nose (Cyranose C320). Statistical analysis of the sensor responses was performed off-line using principal component (PCA) analyses, discriminant analysis (CDA), and ROC curves. Principal components best discriminating groups were identified with univariable ANOVA analysis. groups were identified with univariable ANOVA analysis. Here, 110/133 and 123/139 cases were correctly identified in the PCa and healthy control cohorts, respectively (sensitivity 82.7%, specificity 88.5%; positive predictive value 87.3%, negative predictive value 84.2%). The Cross Validated Accuracy (CVA 85.3%, p < 0.001) was calculated. Using ROC analysis, the area under the curve was 0.9. Urine volatilome profiling via an electronic nose seems a promising non-invasive diagnostic tool. Full article

(This article belongs to the Special Issue Non-invasive Methods for Screening, Prognosis, and Prediction in Prostate Cancers)

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22 pages, 16882 KiB

Open AccessArticle

Computational Detection of Extraprostatic Extension of Prostate Cancer on Multiparametric MRI Using Deep Learning

by Ştefania L. Moroianu, Indrani Bhattacharya, Arun Seetharaman, Wei Shao, Christian A. Kunder, Avishkar Sharma, Pejman Ghanouni, Richard E. Fan, Geoffrey A. Sonn and Mirabela Rusu

Cancers 2022, 14(12), 2821; https://doi.org/10.3390/cancers14122821 - 07 Jun 2022

Cited by 7 | Viewed by 3068

Abstract

The localization of extraprostatic extension (EPE), i.e., local spread of prostate cancer beyond the prostate capsular boundary, is important for risk stratification and surgical planning. However, the sensitivity of EPE detection by radiologists on MRI is low (57% on average). In this paper, we propose a method for computational detection of EPE on multiparametric MRI using deep learning. Ground truth labels of cancers and EPE were obtained in 123 patients (38 with EPE) by registering pre-surgical MRI with whole-mount digital histopathology images from radical prostatectomy. Our approach has two stages. First, we trained deep learning models using the MRI as input to generate cancer probability maps both inside and outside the prostate. Second, we built an image post-processing pipeline that generates predictions for EPE location based on the cancer probability maps and clinical knowledge. We used five-fold cross-validation to train our approach using data from 74 patients and tested it using data from an independent set of 49 patients. We compared two deep learning models for cancer detection: (i) UNet and (ii) the Correlated Signature Network for Indolent and Aggressive prostate cancer detection (CorrSigNIA). The best end-to-end model for EPE detection, which we call EPENet, was based on the CorrSigNIA cancer detection model. EPENet was successful at detecting cancers with extraprostatic extension, achieving a mean area under the receiver operator characteristic curve of

0.72

at the patient-level. On the test set, EPENet had 80.0% sensitivity and 28.2% specificity at the patient-level compared to 50.0% sensitivity and 76.9% specificity for the radiologists. To account for spatial location of predictions during evaluation, we also computed results at the sextant-level, where the prostate was divided into sextants according to standard systematic 12-core biopsy procedure. At the sextant-level, EPENet achieved mean sensitivity 61.1% and mean specificity 58.3%. Our approach has the potential to provide the location of extraprostatic extension using MRI alone, thus serving as an independent diagnostic aid to radiologists and facilitating treatment planning. Full article

(This article belongs to the Special Issue Non-invasive Methods for Screening, Prognosis, and Prediction in Prostate Cancers)

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9 pages, 1563 KiB

Open AccessArticle

Comparison of Proclarix, PSA Density and MRI-ERSPC Risk Calculator to Select Patients for Prostate Biopsy after mpMRI

by Miriam Campistol, Juan Morote, Marina Triquell, Lucas Regis, Ana Celma, Inés de Torres, María E. Semidey, Richard Mast, Anna Santamaría, Jacques Planas and Enrique Trilla

Cancers 2022, 14(11), 2702; https://doi.org/10.3390/cancers14112702 - 30 May 2022

Cited by 3 | Viewed by 2156

Abstract

Tools to properly select candidates for prostate biopsy after magnetic resonance imaging (MRI) have usually been analyzed in overall populations with suspected prostate cancer (PCa). However, the performance of these tools can change regarding the Prostate Imaging-Reporting and Data System (PI-RADS) categories due to the different incidence of clinically significant PCa (csPCa). The objective of the study was to analyze PSA density (PSAD), MRI-ERSPC risk calculator (RC), and Proclarix to properly select candidates for prostate biopsy regarding PI-RADS categories. We performed a head-to-head analysis of 567 men with suspected PCa, PSA > 3 ng/mL and/or abnormal rectal examination, in whom two to four core transrectal ultrasound (TRUS) guided biopsies to PI-RADS ≥ three lesions and/or 12-core TRUS systematic biopsies were performed after 3-tesla mpMRI between January 2018 and March 2020 in one academic institution. The overall detection of csPCa was 40.9% (6% in PI-RADS < 3, 14.8% in PI-RADS 3, 55.3% in PI-RADS 4, and 88.9% in PI-RADS 5). MRI-ERSPC model exhibited a net benefit over PSAD and Proclarix in the overall population. Proclarix outperformed PSAD and MRI-ERSPC RC in PI-RADS ≤ 3. PSAD outperformed MRI-ESRPC RC and Proclarix in PI-RADS > 3, although none of them exhibited 100% sensitivity for csPCa in this setting. Therefore, tools to properly select candidates for prostate biopsy after MRI must be analyzed regarding the PI-RADS categories. While MRI-ERSPC RC outperformed PSAD and Proclarix in the overall population, Proclarix outperformed in PI-RADS ≤ 3, and no tool guaranteed 100% detection of csPCa in PI-RADS 4 and 5. Full article

(This article belongs to the Special Issue Non-invasive Methods for Screening, Prognosis, and Prediction in Prostate Cancers)

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22 pages, 3739 KiB

Open AccessArticle

Comparative Analysis of PSA Density and an MRI-Based Predictive Model to Improve the Selection of Candidates for Prostate Biopsy

by Juan Morote, Angel Borque-Fernando, Marina Triquell, Anna Celma, Lucas Regis, Richard Mast, Inés M. de Torres, María E. Semidey, José M. Abascal, Pol Servian, Anna Santamaría, Jacques Planas, Luis M. Esteban and Enrique Trilla

Cancers 2022, 14(10), 2374; https://doi.org/10.3390/cancers14102374 - 11 May 2022

Cited by 3 | Viewed by 2217

Abstract

This study is a head-to-head comparison between mPSAD and MRI-PMbdex. The MRI-PMbdex was created from 2432 men with suspected PCa; this cohort comprised the development and external validation cohorts of the Barcelona MRI predictive model. Pre-biopsy 3-Tesla multiparametric MRI (mpMRI) and 2 to 4-core transrectal ultrasound (TRUS)-guided biopsies for suspicious lesions and/or 12-core TRUS systematic biopsies were scheduled. Clinically significant PCa (csPCa), defined as Gleason-based Grade Group 2 or higher, was detected in 934 men (38.4%). The area under the curve was 0.893 (95% confidence interval [CI]: 0.880–0.906) for MRI-PMbdex and 0.764 (95% CI: 0.774–0.783) for mPSAD, with p < 0.001. MRI-PMbdex showed net benefit over biopsy in all men when the probability of csPCa was greater than 2%, while mPSAD did the same when the probability of csPCa was greater than 18%. Thresholds of 13.5% for MRI-PMbdex and 0.628 ng/mL² for mPSAD had 95% sensitivity for csPCa and presented 51.1% specificity for MRI-PMbdex and 19.6% specificity for mPSAD, with p < 0.001. MRI-PMbdex exhibited net benefit over mPSAD in men with prostate imaging report and data system (PI-RADS) <4, while neither exhibited any benefit in men with PI-RADS 5. Hence, we can conclude that MRI-PMbdex is more accurate than mPSAD for the proper selection of candidates for prostate biopsy among men with suspected PCa, with the exception of men with a PI-RAD S 5 score, for whom neither tool exhibited clinical guidance to determine the need for biopsy. Full article

(This article belongs to the Special Issue Non-invasive Methods for Screening, Prognosis, and Prediction in Prostate Cancers)

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Review

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16 pages, 599 KiB

Open AccessReview

Integration between Novel Imaging Technologies and Modern Radiotherapy Techniques: How the Eye Drove the Chisel

by Giulio Francolini, Ilaria Morelli, Maria Grazia Carnevale, Roberta Grassi, Valerio Nardone, Mauro Loi, Marianna Valzano, Viola Salvestrini, Lorenzo Livi and Isacco Desideri

Cancers 2022, 14(21), 5277; https://doi.org/10.3390/cancers14215277 - 27 Oct 2022

Cited by 2 | Viewed by 1275

Abstract

Introduction: Targeted dose-escalation and reduction of dose to adjacent organs at risk have been the main goal of radiotherapy in the last decade. Prostate cancer benefited the most from this process. In recent years, the development of Intensity Modulated Radiation Therapy (IMRT) and Stereotactic Body Radiotherapy (SBRT) radically changed clinical practice, also thanks to the availability of modern imaging techniques. The aim of this paper is to explore the relationship between diagnostic imaging and prostate cancer radiotherapy techniques. Materials and Methods: Aiming to provide an overview of the integration between modern imaging and radiotherapy techniques, we performed a non-systematic search of papers exploring the predictive value of imaging before treatment, the role of radiomics in predicting treatment outcomes, implementation of novel imaging in RT planning and influence of imaging integration on use of RT in current clinical practice. Three independent authors (GF, IM and ID) performed an independent review focusing on these issues. Key references were derived from a PubMed query. Hand searching and clinicaltrials.gov were also used, and grey literature was searched for further papers of interest. The final choice of papers included was discussed between all co-authors. Results: This paper contains a narrative report and a critical discussion of the role of new modern techniques in predicting outcomes before treatment, in radiotherapy planning and in the integration with systemic therapy in the management of prostate cancer. Also, the role of radiomics in a tailored treatment approach is explored. Conclusions: Integration between diagnostic imaging and radiotherapy is of great importance for the modern treatment of prostate cancer. Future clinical trials should be aimed at exploring the real clinical benefit of complex workflows in clinical practice. Full article

(This article belongs to the Special Issue Non-invasive Methods for Screening, Prognosis, and Prediction in Prostate Cancers)

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7 pages, 249 KiB

Open AccessEditor’s ChoiceReview

Defining Oligometastatic Disease in the New Era of PSMA-PET Imaging for Primary Staging of Prostate Cancer

by Samuel J. Galgano, Andrew M. McDonald, Janelle T. West and Soroush Rais-Bahrami

Cancers 2022, 14(14), 3302; https://doi.org/10.3390/cancers14143302 - 06 Jul 2022

Cited by 10 | Viewed by 2003

Abstract

Oligometastatic prostate cancer has traditionally been defined in the literature as a limited number of metastatic lesions (either to soft tissue or bone), typically based on findings seen on CT, MRI, and skeletal scintigraphy. Although definitions have varied among research studies, many important clinical trials have documented effective treatments and prognostication in patients with oligometastatic prostate cancer. In current clinical practice, prostate-specific membrane antigen (PSMA)-PET/CT is increasingly utilized for the initial staging of high-risk patients and, in many cases, detecting metastases that would have otherwise been undetected with conventional staging imaging. Thus, patients with presumed localized and/or oligometastatic prostate cancer undergo stage migration based on more novel molecular imaging. As a result, it is challenging to apply the data from the era before widespread PET utilization to current clinical practice and to relate current trials using PSMA-PET/CT for disease detection to older studies using conventional staging imaging alone. This manuscript aims to review the definition of oligometastatic prostate cancer, summarize important studies utilizing both PSMA-PET/CT and conventional anatomic imaging, discuss the concept of stage migration, and discuss current problems and challenges with the current definition of oligometastatic disease. Full article

(This article belongs to the Special Issue Non-invasive Methods for Screening, Prognosis, and Prediction in Prostate Cancers)

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Non-invasive Methods for Screening, Prognosis, and Prediction in Prostate Cancers

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