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Diagnostics
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Advances in the Diagnosis of Prostate Cancer
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Advances in the Diagnosis of 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 November 2022) | Viewed by 3391

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

Dear Colleagues,

Magnetic resonance imaging (MRI) has gained importance in the management of patients with prostate cancer, and its potential for use as a triage test before prostate biopsy has recently been demonstrated. Moreover, the application of radiomics and artificial intelligence (AI) in prostate cancer care is 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

Published Papers (2 papers)

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Research

13 pages, 2898 KiB  
Article
MRI-Based Radiomics Nomogram for Predicting Prostate Cancer with Gray-Zone Prostate-Specific Antigen Levels to Reduce Unnecessary Biopsies
by Li Zhang, Jing Zhang, Min Tang, Xiao-Yan Lei and Long-Chao Li
Diagnostics 2022, 12(12), 3005; https://doi.org/10.3390/diagnostics12123005 - 01 Dec 2022
Cited by 3 | Viewed by 1310
Abstract
Objective: The aim of this study was to establish a predictive nomogram for predicting prostate cancer (PCa) in patients with gray-zone prostate-specific antigen (PSA) levels (4–10.0 ng/mL) based on radiomics and other traditional clinical parameters. Methods: In all, 274 patients with gray-zone PSA [...] Read more.
Objective: The aim of this study was to establish a predictive nomogram for predicting prostate cancer (PCa) in patients with gray-zone prostate-specific antigen (PSA) levels (4–10.0 ng/mL) based on radiomics and other traditional clinical parameters. Methods: In all, 274 patients with gray-zone PSA levels were included in this retrospective study. They were randomly divided into training and validation sets (n = 191 and 83, respectively). Data on the clinical risk factors related to PCa with gray-zone PSA levels (such as Prostate Imaging Reporting and Data System, version 2.1 [PI-RADS V2.1] category, age, prostate volume, and serum PSA level) were collected for all patients. Lesion volumes of interest (VOI) from T2-weighted imaging (T2WI) and apparent diffusion coefficient (ADC) imaging were annotated by two radiologists. The radiomics model, clinical model, and combined prediction model, which was presented on a nomogram by incorporating the radiomics signature and clinical and radiological risk factors for PCa, were developed using logistic regression. The area under the receiver operator characteristic (AUC-ROC) and decision, calibration curve were used to compare the three models for the diagnosis of PCa with gray-zone PSA levels. Results: The predictive nomogram (AUC: 0.953) incorporating the radiomics score and PI-RADS V2.1 category, age, and the radiomics model (AUC: 0.941) afforded much higher diagnostic efficacy than the clinical model (AUC: 0.866). The addition of the rad score could improve the discriminatory performance of the clinical model. The decision curve analysis indicated that the radiomics or combined model could be more beneficial compared to the clinical model for the prediction of PCa. The nomogram showed good agreement for detecting PCa with gray-zone PSA levels between prediction and histopathologic confirmation. Conclusion: The nomogram, which combined the radiomics score and PI-RADS V2.1 category and age, is an effective and non-invasive method for predicting PCa. Furthermore, as well as good calibration and is clinically useful, which could reduce unnecessary prostate biopsies in patients having PCa with gray-zone PSA levels. Full article
(This article belongs to the Special Issue Advances in the Diagnosis of Prostate Cancer)
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10 pages, 2542 KiB  
Article
Improved Visualization of Prostate Cancer Using Multichannel Computed Diffusion Images: Combining ADC and DWI
by Matthias Hammon, Marc Saake, Frederik B. Laun, Rafael Heiss, Nicola Seuss, Rolf Janka, Alexander Cavallaro, Michael Uder and Hannes Seuss
Diagnostics 2022, 12(7), 1592; https://doi.org/10.3390/diagnostics12071592 - 30 Jun 2022
Cited by 1 | Viewed by 1475
Abstract
(1) Background: For the peripheral zone of the prostate, diffusion weighted imaging (DWI) is the most important MRI technique; however, a high b-value image (hbDWI) must always be evaluated in conjunction with an apparent diffusion coefficient (ADC) map. We aimed to unify the [...] Read more.
(1) Background: For the peripheral zone of the prostate, diffusion weighted imaging (DWI) is the most important MRI technique; however, a high b-value image (hbDWI) must always be evaluated in conjunction with an apparent diffusion coefficient (ADC) map. We aimed to unify the important contrast features of both a hbDWI and ADC in one single image, termed multichannel computed diffusion images (mcDI), and evaluate the values of these images in a retrospective clinical study; (2) Methods: Based on the 2D histograms of hbDWI and ADC images of 70 patients with histologically proven prostate cancer (PCa) in the peripheral zone, an algorithm was designed to generate the mcDI. Then, three radiologists evaluated the data of 56 other patients twice in three settings (T2w images +): (1) hbDWI and ADC; (2) mcDI; and (3) mcDI, hbDWI, and ADC. The sensitivity, specificity, and inter-reader variability were evaluated; (3) Results: The overall sensitivity/specificity were 0.91/0.78 (hbDWI + ADC), 0.85/0.88 (mcDI), and 0.97/0.88 (mcDI + hbDWI + ADC). The kappa-values for the inter-reader variability were 0.732 (hbDWI + ADC), 0.800 (mcDI), and 0.853 (mcDI + hbDWI + ADC). (4) Conclusions: By using mcDI, the specificity of the MRI detection of PCa was increased at the expense of the sensitivity. By combining the conventional diffusion data with the mcDI data, both the sensitivity and specificity were improved. Full article
(This article belongs to the Special Issue Advances in the Diagnosis of Prostate Cancer)
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