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Diagnostics
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Role of Imaging and Artificial Intelligence in Prostate Cancer
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Role of Imaging and Artificial Intelligence in Prostate Cancer

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

Deadline for manuscript submissions: closed (30 June 2021) | Viewed by 27058

Special Issue Editor

Dr. Valentina Giannini

E-Mail Website
Guest Editor
1. Department of Surgical Sciences, University of Turin, 10126 Turin, Italy
2. Department of Radiology, Candiolo Cancer Institute, FPO-IRCCS, 10060 Candiolo, Italy
Interests: radiomics; artificial intelligence; computer-aided diagnosis (CAD) systems; medical imaging
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Magnetic resonance imaging (MRI) has gained importance in the management of patients with prostate cancer, and recently, its potential to be used as a triage test before a prostate biopsy has been demonstrated. Moreover, radiomics and artificial intelligence (AI) applied to prostate cancer care are rapidly growing to improve the detection and characterization of prostate cancer and risk stratification.

The purpose of this Special Issue is to analyze how MRI alone or combined with AI can contribute to enhancing health-care delivery by enabling the use of customized precision-care pathways. We encourage the submission of studies that are aimed at optimizing the accuracy and reproducibility of prostate cancer detection and that can help clinicians by potentially reducing the chances of either missing or overdiagnosing suspicious targets on diagnostic MRI.

Dr. Valentina Giannini
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Diagnostics is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • magnetic resonance imaging
  • artificial intelligence
  • computer-aided diagnosis
  • prostate cancer detection
  • prostate cancer characterization
  • prognostic biomarkers
  • diagnostic biomarkers
  • imaging biomarkers
  • personalized medicine

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Published Papers (7 papers)

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Research

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13 pages, 2249 KiB  
Article
Computer-Aided Diagnosis Improves the Detection of Clinically Significant Prostate Cancer on Multiparametric-MRI: A Multi-Observer Performance Study Involving Inexperienced Readers
by Valentina Giannini, Simone Mazzetti, Giovanni Cappello, Valeria Maria Doronzio, Lorenzo Vassallo, Filippo Russo, Alessandro Giacobbe, Giovanni Muto and Daniele Regge
Diagnostics 2021, 11(6), 973; https://doi.org/10.3390/diagnostics11060973 - 28 May 2021
Cited by 12 | Viewed by 2573
Abstract
Recently, Computer Aided Diagnosis (CAD) systems have been proposed to help radiologists in detecting and characterizing Prostate Cancer (PCa). However, few studies evaluated the performances of these systems in a clinical setting, especially when used by non-experienced readers. The main aim of this [...] Read more.
Recently, Computer Aided Diagnosis (CAD) systems have been proposed to help radiologists in detecting and characterizing Prostate Cancer (PCa). However, few studies evaluated the performances of these systems in a clinical setting, especially when used by non-experienced readers. The main aim of this study is to assess the diagnostic performance of non-experienced readers when reporting assisted by the likelihood map generated by a CAD system, and to compare the results with the unassisted interpretation. Three resident radiologists were asked to review multiparametric-MRI of patients with and without PCa, both unassisted and assisted by a CAD system. In both reading sessions, residents recorded all positive cases, and sensitivity, specificity, negative and positive predictive values were computed and compared. The dataset comprised 90 patients (45 with at least one clinically significant biopsy-confirmed PCa). Sensitivity significantly increased in the CAD assisted mode for patients with at least one clinically significant lesion (GS > 6) (68.7% vs. 78.1%, p = 0.018). Overall specificity was not statistically different between unassisted and assisted sessions (94.8% vs. 89.6, p = 0.072). The use of the CAD system significantly increases the per-patient sensitivity of inexperienced readers in the detection of clinically significant PCa, without negatively affecting specificity, while significantly reducing overall reporting time. Full article
(This article belongs to the Special Issue Role of Imaging and Artificial Intelligence in Prostate Cancer)
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11 pages, 1824 KiB  
Article
Diffusion Is Directional: Innovative Diffusion Tensor Imaging to Improve Prostate Cancer Detection
by Chen Shenhar, Hadassa Degani, Yaara Ber, Jack Baniel, Shlomit Tamir, Ofer Benjaminov, Philip Rosen, Edna Furman-Haran and David Margel
Diagnostics 2021, 11(3), 563; https://doi.org/10.3390/diagnostics11030563 - 20 Mar 2021
Cited by 9 | Viewed by 2849
Abstract
In the prostate, water diffusion is faster when moving parallel to duct and gland walls than when moving perpendicular to them, but these data are not currently utilized in multiparametric magnetic resonance imaging (mpMRI) for prostate cancer (PCa) detection. Diffusion tensor imaging (DTI) [...] Read more.
In the prostate, water diffusion is faster when moving parallel to duct and gland walls than when moving perpendicular to them, but these data are not currently utilized in multiparametric magnetic resonance imaging (mpMRI) for prostate cancer (PCa) detection. Diffusion tensor imaging (DTI) can quantify the directional diffusion of water in tissue and is applied in brain and breast imaging. Our aim was to determine whether DTI may improve PCa detection. We scanned patients undergoing mpMRI for suspected PCa with a DTI sequence. We calculated diffusion metrics from DTI and diffusion weighted imaging (DWI) for suspected lesions and normal-appearing prostate tissue, using specialized software for DTI analysis, and compared predictive values for PCa in targeted biopsies, performed when clinically indicated. DTI scans were performed on 78 patients, 42 underwent biopsy and 16 were diagnosed with PCa. The median age was 62 (IQR 54.4–68.4), and PSA 4.8 (IQR 1.3–10.7) ng/mL. DTI metrics distinguished PCa lesions from normal tissue. The prime diffusion coefficient (λ1) was lower in both peripheral-zone (p < 0.0001) and central-gland (p < 0.0001) cancers, compared to normal tissue. DTI had higher negative and positive predictive values than mpMRI to predict PCa (positive predictive value (PPV) 77.8% (58.6–97.0%), negative predictive value (NPV) 91.7% (80.6–100%) vs. PPV 46.7% (28.8–64.5%), NPV 83.3% (62.3–100%)). We conclude from this pilot study that DTI combined with T2-weighted imaging may have the potential to improve PCa detection without requiring contrast injection. Full article
(This article belongs to the Special Issue Role of Imaging and Artificial Intelligence in Prostate Cancer)
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14 pages, 4031 KiB  
Article
Lesion-Based Bone Metastasis Detection in Chest Bone Scintigraphy Images of Prostate Cancer Patients Using Pre-Train, Negative Mining, and Deep Learning
by Da-Chuan Cheng, Te-Chun Hsieh, Kuo-Yang Yen and Chia-Hung Kao
Diagnostics 2021, 11(3), 518; https://doi.org/10.3390/diagnostics11030518 - 15 Mar 2021
Cited by 36 | Viewed by 4378
Abstract
This study aimed to explore efficient ways to diagnose bone metastasis early using bone scintigraphy images through negative mining, pre-training, the convolutional neural network, and deep learning. We studied 205 prostate cancer patients and 371 breast cancer patients and used bone scintigraphy data [...] Read more.
This study aimed to explore efficient ways to diagnose bone metastasis early using bone scintigraphy images through negative mining, pre-training, the convolutional neural network, and deep learning. We studied 205 prostate cancer patients and 371 breast cancer patients and used bone scintigraphy data from breast cancer patients to pre-train a YOLO v4 with a false-positive reduction strategy. With the pre-trained model, transferred learning was applied to prostate cancer patients to build a model to detect and identify metastasis locations using bone scintigraphy. Ten-fold cross validation was conducted. The mean sensitivity and precision rates for bone metastasis location detection and classification (lesion-based) in the chests of prostate patients were 0.72 ± 0.04 and 0.90 ± 0.04, respectively. The mean sensitivity and specificity rates for bone metastasis classification (patient-based) in the chests of prostate patients were 0.94 ± 0.09 and 0.92 ± 0.09, respectively. The developed system has the potential to provide pre-diagnostic reports to aid in physicians’ final decisions. Full article
(This article belongs to the Special Issue Role of Imaging and Artificial Intelligence in Prostate Cancer)
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12 pages, 3327 KiB  
Article
Associations between Statin/Omega3 Usage and MRI-Based Radiomics Signatures in Prostate Cancer
by Yu Shi, Ethan Wahle, Qian Du, Luke Krajewski, Xiaoying Liang, Sumin Zhou, Chi Zhang, Michael Baine and Dandan Zheng
Diagnostics 2021, 11(1), 85; https://doi.org/10.3390/diagnostics11010085 - 7 Jan 2021
Cited by 5 | Viewed by 3020
Abstract
Prostate cancer is the most common noncutaneous cancer and the second leading cause of cancer deaths among American men. Statins and omega-3 are two medications recently found to correlate with prostate cancer risk and aggressiveness, but the observed associations are complex and controversial. [...] Read more.
Prostate cancer is the most common noncutaneous cancer and the second leading cause of cancer deaths among American men. Statins and omega-3 are two medications recently found to correlate with prostate cancer risk and aggressiveness, but the observed associations are complex and controversial. We therefore explore the novel application of radiomics in studying statin and omega-3 usage in prostate cancer patients. On MRIs of 91 prostate cancer patients, two regions of interest (ROIs), the whole prostate and the peripheral region of the prostate, were manually segmented. From each ROI, 944 radiomic features were extracted after field bias correction and normalization. Heatmaps were generated to study the radiomic feature patterns against statin or omega-3 usage. Radiomics models were trained on selected features and evaluated with 500-round threefold cross-validation for each drug/ROI combination. On the 1500 validation datasets, the radiomics model achieved average AUCs of 0.70, 0.74, 0.78, and 0.72 for omega-3/prostate, omega-3/peripheral, statin/prostate, and statin/peripheral, respectively. As the first study to analyze radiomics in relation to statin and omega-3 uses in prostate cancer patients, our study preliminarily established the existence of imaging-identifiable tissue-level changes in the prostate and illustrated the potential usefulness of radiomics for further exploring these medications’ effects and mechanisms in prostate cancer. Full article
(This article belongs to the Special Issue Role of Imaging and Artificial Intelligence in Prostate Cancer)
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11 pages, 1402 KiB  
Article
A Deep Learning-Based Automated CT Segmentation of Prostate Cancer Anatomy for Radiation Therapy Planning-A Retrospective Multicenter Study
by Timo Kiljunen, Saad Akram, Jarkko Niemelä, Eliisa Löyttyniemi, Jan Seppälä, Janne Heikkilä, Kristiina Vuolukka, Okko-Sakari Kääriäinen, Vesa-Pekka Heikkilä, Kaisa Lehtiö, Juha Nikkinen, Eduard Gershkevitsh, Anni Borkvel, Merve Adamson, Daniil Zolotuhhin, Kati Kolk, Eric Pei Ping Pang, Jeffrey Kit Loong Tuan, Zubin Master, Melvin Lee Kiang Chua, Timo Joensuu, Juha Kononen, Mikko Myllykangas, Maigo Riener, Miia Mokka and Jani Keyriläinenadd Show full author list remove Hide full author list
Diagnostics 2020, 10(11), 959; https://doi.org/10.3390/diagnostics10110959 - 17 Nov 2020
Cited by 48 | Viewed by 6569
Abstract
A commercial deep learning (DL)-based automated segmentation tool (AST) for computed tomography (CT) is evaluated for accuracy and efficiency gain within prostate cancer patients. Thirty patients from six clinics were reviewed with manual- (MC), automated- (AC) and automated and edited (AEC) contouring methods. [...] Read more.
A commercial deep learning (DL)-based automated segmentation tool (AST) for computed tomography (CT) is evaluated for accuracy and efficiency gain within prostate cancer patients. Thirty patients from six clinics were reviewed with manual- (MC), automated- (AC) and automated and edited (AEC) contouring methods. In the AEC group, created contours (prostate, seminal vesicles, bladder, rectum, femoral heads and penile bulb) were edited, whereas the MC group included empty datasets for MC. In one clinic, lymph node CTV delineations were evaluated for interobserver variability. Compared to MC, the mean time saved using the AST was 12 min for the whole data set (46%) and 12 min for the lymph node CTV (60%), respectively. The delineation consistency between MC and AEC groups according to the Dice similarity coefficient (DSC) improved from 0.78 to 0.94 for the whole data set and from 0.76 to 0.91 for the lymph nodes. The mean DSCs between MC and AC for all six clinics were 0.82 for prostate, 0.72 for seminal vesicles, 0.93 for bladder, 0.84 for rectum, 0.69 for femoral heads and 0.51 for penile bulb. This study proves that using a general DL-based AST for CT images saves time and improves consistency. Full article
(This article belongs to the Special Issue Role of Imaging and Artificial Intelligence in Prostate Cancer)
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14 pages, 1503 KiB  
Article
Autonomous Detection and Classification of PI-RADS Lesions in an MRI Screening Population Incorporating Multicenter-Labeled Deep Learning and Biparametric Imaging: Proof of Concept
by David J. Winkel, Christian Wetterauer, Marc Oliver Matthias, Bin Lou, Bibo Shi, Ali Kamen, Dorin Comaniciu, Hans-Helge Seifert, Cyrill A. Rentsch and Daniel T. Boll
Diagnostics 2020, 10(11), 951; https://doi.org/10.3390/diagnostics10110951 - 14 Nov 2020
Cited by 36 | Viewed by 3686
Abstract
Background: Opportunistic prostate cancer (PCa) screening is a controversial topic. Magnetic resonance imaging (MRI) has proven to detect prostate cancer with a high sensitivity and specificity, leading to the idea to perform an image-guided prostate cancer (PCa) screening; Methods: We evaluated a prospectively [...] Read more.
Background: Opportunistic prostate cancer (PCa) screening is a controversial topic. Magnetic resonance imaging (MRI) has proven to detect prostate cancer with a high sensitivity and specificity, leading to the idea to perform an image-guided prostate cancer (PCa) screening; Methods: We evaluated a prospectively enrolled cohort of 49 healthy men participating in a dedicated image-guided PCa screening trial employing a biparametric MRI (bpMRI) protocol consisting of T2-weighted (T2w) and diffusion weighted imaging (DWI) sequences. Datasets were analyzed both by human readers and by a fully automated artificial intelligence (AI) software using deep learning (DL). Agreement between the algorithm and the reports—serving as the ground truth—was compared on a per-case and per-lesion level using metrics of diagnostic accuracy and k statistics; Results: The DL method yielded an 87% sensitivity (33/38) and 50% specificity (5/10) with a k of 0.42. 12/28 (43%) Prostate Imaging Reporting and Data System (PI-RADS) 3, 16/22 (73%) PI-RADS 4, and 5/5 (100%) PI-RADS 5 lesions were detected compared to the ground truth. Targeted biopsy revealed PCa in six participants, all correctly diagnosed by both the human readers and AI. Conclusions: The results of our study show that in our AI-assisted, image-guided prostate cancer screening the software solution was able to identify highly suspicious lesions and has the potential to effectively guide the targeted-biopsy workflow. Full article
(This article belongs to the Special Issue Role of Imaging and Artificial Intelligence in Prostate Cancer)
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Review

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11 pages, 264 KiB  
Review
MRI and Targeted Biopsy Essential Tools for an Accurate Diagnosis and Treatment Decision Making in Prostate Cancer
by Suraj Samtani, Mauricio Burotto, Juan Carlos Roman, Daniela Cortes-Herrera and Annerleim Walton-Diaz
Diagnostics 2021, 11(9), 1551; https://doi.org/10.3390/diagnostics11091551 - 27 Aug 2021
Cited by 2 | Viewed by 2773
Abstract
Prostate cancer (PCa) is one of the most frequent causes of cancer death worldwide. Historically, diagnosis was based on physical examination, transrectal (TRUS) images, and TRUS biopsy resulting in overdiagnosis and overtreatment. Recently magnetic resonance imaging (MRI) has been identified as an evolving [...] Read more.
Prostate cancer (PCa) is one of the most frequent causes of cancer death worldwide. Historically, diagnosis was based on physical examination, transrectal (TRUS) images, and TRUS biopsy resulting in overdiagnosis and overtreatment. Recently magnetic resonance imaging (MRI) has been identified as an evolving tool in terms of diagnosis, staging, treatment decision, and follow-up. In this review we provide the key studies and concepts of MRI as a promising tool in the diagnosis and management of prostate cancer in the general population and in challenging scenarios, such as anteriorly located lesions, enlarged prostates determining extracapsular extension and seminal vesicle invasion, and prior negative biopsy and the future role of MRI in association with artificial intelligence (AI). Full article
(This article belongs to the Special Issue Role of Imaging and Artificial Intelligence in Prostate Cancer)
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