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Cancers
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New Technologies in Prostate Cancer: From Diagnosis to Treatment
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New Technologies in Prostate Cancer: From Diagnosis to Treatment

A special issue of Cancers (ISSN 2072-6694). This special issue belongs to the section "Methods and Technologies Development".

Deadline for manuscript submissions: closed (31 July 2022) | Viewed by 16258

Special Issue Editor

Dr. Fabio Zattoni

E-Mail Website
Guest Editor
1. Department of Urology, Azienda Ospedale-Università Padova, 35122 Padova, Italy
2. Department of Medicine (DIMED), University of Padua, 35128 Padova, Italy
Interests: prostate cancer; biopsy
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Prostate cancer is the second-most deadly cancer in men with more than 30,000 deaths a year in the United States. It is also very common, affecting 1 in 9 men, and decisions about treatment can sometimes be complex. Novel diagnostic strategies and newer available therapeutic technology will facilitate the development of futuristic ways to diagnose and to cure patients. This will have implications in enhancing patients’ communications with telemedicine, training, tumor staging, surgical approach and surgical techniques, with the final aim to improve tumor detection, characterization and treatments.

This Special Issue aims to explore cutting-edge, new technologies that may, in the near future, be able to change the current diagnostic and treatment pathways of prostate cancer.

Artificial intelligence allows us to recognize difficult relationships and manage enormous data sets, which will reduce the level of subjectivity in imaging evaluation. There has been a growing interest in the automatic extraction of quantitative features from medical images, denoted as radiomics.

New imaging modalities, such as PET/MRI, may have a role in the diagnosis and restaging of prostate cancer.

New tracers and new tumor markers have shown promising results in guiding the treatment of metastatic prostate cancer.

The future of robotic surgery will be also investigated, as well as new mini-invasive ways to treat prostate cancer, from focal treatment technologies to theragnostics.

In this Special Issue, original research articles and reviews are welcome. Research areas may include (but are not limited to) the following:

  • Artificial intelligence;
  • Radiomics;
  • PET/MRI;
  • New PET tracers;
  • Liquid biopsy;
  • New genomic markers;
  • Telemedicine;
  • Augmented reality;
  • New and developing robotic systems;
  • Single-site surgery;
  • Telerobotic surgery;
  • Radio-guided surgery;
  • Focal therapies;
  • Theragnostics.

Dr. Fabio Zattoni
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 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

  • prostate cancer
  • robotic surgery
  • prostate imaging
  • focal therapy
  • theragnostics

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Published Papers (4 papers)

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Research

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21 pages, 2925 KiB  
Article
AutoProstate: Towards Automated Reporting of Prostate MRI for Prostate Cancer Assessment Using Deep Learning
by Pritesh Mehta, Michela Antonelli, Saurabh Singh, Natalia Grondecka, Edward W. Johnston, Hashim U. Ahmed, Mark Emberton, Shonit Punwani and Sébastien Ourselin
Cancers 2021, 13(23), 6138; https://doi.org/10.3390/cancers13236138 - 6 Dec 2021
Cited by 14 | Viewed by 5146
Abstract
Multiparametric magnetic resonance imaging (mpMRI) of the prostate is used by radiologists to identify, score, and stage abnormalities that may correspond to clinically significant prostate cancer (CSPCa). Automatic assessment of prostate mpMRI using artificial intelligence algorithms may facilitate a reduction in missed cancers [...] Read more.
Multiparametric magnetic resonance imaging (mpMRI) of the prostate is used by radiologists to identify, score, and stage abnormalities that may correspond to clinically significant prostate cancer (CSPCa). Automatic assessment of prostate mpMRI using artificial intelligence algorithms may facilitate a reduction in missed cancers and unnecessary biopsies, an increase in inter-observer agreement between radiologists, and an improvement in reporting quality. In this work, we introduce AutoProstate, a deep learning-powered framework for automatic MRI-based prostate cancer assessment. AutoProstate comprises of three modules: Zone-Segmenter, CSPCa-Segmenter, and Report-Generator. Zone-Segmenter segments the prostatic zones on T2-weighted imaging, CSPCa-Segmenter detects and segments CSPCa lesions using biparametric MRI, and Report-Generator generates an automatic web-based report containing four sections: Patient Details, Prostate Size and PSA Density, Clinically Significant Lesion Candidates, and Findings Summary. In our experiment, AutoProstate was trained using the publicly available PROSTATEx dataset, and externally validated using the PICTURE dataset. Moreover, the performance of AutoProstate was compared to the performance of an experienced radiologist who prospectively read PICTURE dataset cases. In comparison to the radiologist, AutoProstate showed statistically significant improvements in prostate volume and prostate-specific antigen density estimation. Furthermore, AutoProstate matched the CSPCa lesion detection sensitivity of the radiologist, which is paramount, but produced more false positive detections. Full article
(This article belongs to the Special Issue New Technologies in Prostate Cancer: From Diagnosis to Treatment)
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12 pages, 1572 KiB  
Article
Development of a Radiomic-Based Model Predicting Lymph Node Involvement in Prostate Cancer Patients
by Vincent Bourbonne, Vincent Jaouen, Truong An Nguyen, Valentin Tissot, Laurent Doucet, Mathieu Hatt, Dimitris Visvikis, Olivier Pradier, Antoine Valéri, Georges Fournier and Ulrike Schick
Cancers 2021, 13(22), 5672; https://doi.org/10.3390/cancers13225672 - 12 Nov 2021
Cited by 16 | Viewed by 2397
Abstract
Significant advances in lymph node involvement (LNI) risk modeling in prostate cancer (PCa) have been achieved with the addition of visual interpretation of magnetic resonance imaging (MRI) data, but it is likely that quantitative analysis could further improve prediction models. In this study, [...] Read more.
Significant advances in lymph node involvement (LNI) risk modeling in prostate cancer (PCa) have been achieved with the addition of visual interpretation of magnetic resonance imaging (MRI) data, but it is likely that quantitative analysis could further improve prediction models. In this study, we aimed to develop and internally validate a novel LNI risk prediction model based on radiomic features extracted from preoperative multimodal MRI. All patients who underwent a preoperative MRI and radical prostatectomy with extensive lymph node dissection were retrospectively included in a single institution. Patients were randomly divided into the training (60%) and testing (40%) sets. Radiomic features were extracted from the index tumor volumes, delineated on the apparent diffusion coefficient corrected map and the T2 sequences. A ComBat harmonization method was applied to account for inter-site heterogeneity. A prediction model was trained using a neural network approach (Multilayer Perceptron Network, SPSS v24.0©) combining clinical, radiomic and all features. It was then evaluated on the testing set and compared to the current available models using the Receiver Operative Characteristics and the C-Index. Two hundred and eighty patients were included, with a median age of 65.2 y (45.3–79.6), a mean PSA level of 9.5 ng/mL (1.04–63.0) and 79.6% of ISUP ≥ 2 tumors. LNI occurred in 51 patients (18.2%), with a median number of extracted nodes of 15 (10–19). In the testing set, with their respective cutoffs applied, the Partin, Roach, Yale, MSKCC, Briganti 2012 and 2017 models resulted in a C-Index of 0.71, 0.66, 0.55, 0.67, 0.65 and 0.73, respectively, while our proposed combined model resulted in a C-Index of 0.89 in the testing set. Radiomic features extracted from the preoperative MRI scans and combined with clinical features through a neural network seem to provide added predictive performance compared to state of the art models regarding LNI risk prediction in PCa. Full article
(This article belongs to the Special Issue New Technologies in Prostate Cancer: From Diagnosis to Treatment)
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14 pages, 3992 KiB  
Article
Comparison of 68Ga-Prostate Specific Membrane Antigen (PSMA) Positron Emission Tomography Computed Tomography (PET-CT) and Whole-Body Magnetic Resonance Imaging (WB-MRI) with Diffusion Sequences (DWI) in the Staging of Advanced Prostate Cancer
by Julien Van Damme, Bertrand Tombal, Laurence Collette, Sandy Van Nieuwenhove, Vassiliki Pasoglou, Thomas Gérard, François Jamar, Renaud Lhommel and Frédéric E. Lecouvet
Cancers 2021, 13(21), 5286; https://doi.org/10.3390/cancers13215286 - 21 Oct 2021
Cited by 17 | Viewed by 2891
Abstract
Background: Prostate specific membrane antigen (PSMA) positron emission tomography computed tomography (PET-CT) and whole-body magnetic resonance imaging (WB-MRI) outperform standard imaging technology for the detection of metastasis in prostate cancer (PCa). There are few direct comparisons between both modalities. This paper compares the [...] Read more.
Background: Prostate specific membrane antigen (PSMA) positron emission tomography computed tomography (PET-CT) and whole-body magnetic resonance imaging (WB-MRI) outperform standard imaging technology for the detection of metastasis in prostate cancer (PCa). There are few direct comparisons between both modalities. This paper compares the diagnostic accuracy of PSMA PET-CT and WB-MRI for the detection of metastasis in PCa. One hundred thirty-four patients with newly diagnosed PCa (n = 81) or biochemical recurrence after curative treatment (n = 53) with high-risk features prospectively underwent PSMA PET-CT and WB-MRI. The diagnostic accuracy of both techniques for lymph node, skeletal and visceral metastases was compared against a best valuable comparator (BVC). Overall, no significant difference was detected between PSMA PET-CT and WB-MRI to identify metastatic patients when considering lymph nodes, skeletal and visceral metastases together (AUC = 0.96 (0.92–0.99) vs. 0.90 (0.85–0.95); p = 0.09). PSMA PET-CT, however, outperformed WB-MRI in the subgroup of patients with newly diagnosed PCa for the detection of lymph node metastases (AUC = 0.96 (0.92–0.99) vs. 0.86 (0.79–0.92); p = 0.0096). In conclusion, PSMA PET-CT outperforms WB-MRI for the detection of nodal metastases in primary staging of PCa. Full article
(This article belongs to the Special Issue New Technologies in Prostate Cancer: From Diagnosis to Treatment)
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Review

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19 pages, 3178 KiB  
Review
Raman Spectroscopy in Prostate Cancer: Techniques, Applications and Advancements
by Fortis Gaba, William J. Tipping, Mark Salji, Karen Faulds, Duncan Graham and Hing Y. Leung
Cancers 2022, 14(6), 1535; https://doi.org/10.3390/cancers14061535 - 17 Mar 2022
Cited by 25 | Viewed by 4891
Abstract
Optical techniques are widely used tools in the visualisation of biological species within complex matrices, including biopsies, tissue resections and biofluids. Raman spectroscopy is an emerging analytical approach that probes the molecular signature of endogenous cellular biomolecules under biocompatible conditions with high spatial [...] Read more.
Optical techniques are widely used tools in the visualisation of biological species within complex matrices, including biopsies, tissue resections and biofluids. Raman spectroscopy is an emerging analytical approach that probes the molecular signature of endogenous cellular biomolecules under biocompatible conditions with high spatial resolution. Applications of Raman spectroscopy in prostate cancer include biopsy analysis, assessment of surgical margins and monitoring of treatment efficacy. The advent of advanced Raman imaging techniques, such as stimulated Raman scattering, is creating opportunities for real-time in situ evaluation of prostate cancer. This review provides a focus on the recent preclinical and clinical achievements in implementing Raman-based techniques, highlighting remaining challenges for clinical applications. The research and clinical results achieved through in vivo and ex vivo Raman spectroscopy illustrate areas where these evolving technologies can be best translated into clinical practice. Full article
(This article belongs to the Special Issue New Technologies in Prostate Cancer: From Diagnosis to Treatment)
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