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Diagnostics
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Advanced Imaging in Prostate Cancer Management: Current Status and Future...
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Advanced Imaging in Prostate Cancer Management: Current Status and Future Perspectives

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 3154

Special Issue Editors

Dr. Jing Yuan

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Guest Editor
Research Department, Hong Kong Sanatorium & Hospital, Happy Valley, Hong Kong SAR, China
Interests: MRI; MR-guided radiotherapy; medical imaging
Dr. Darren M. C. Poon

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Guest Editor
Comprehensive Oncology Centre, Hong Kong Sanatorium & Hospital, Happy Valley, Hong Kong SAR, China
Interests: MR-guided radiotherapy; prostate cancer; radiation oncology
Dr. Peter Ka-Fung Chiu

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Guest Editor
Department of Surgery, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong SAR, China
Interests: urology; prostate cancer; surgery
Dr. Garrett Chi Lai Ho

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Guest Editor
Department of Nuclear Medicine and Positron Emission Tomography, Hong Kong Sanatorium & Hospital, Happy Valley, Hong Kong SAR, China
Interests: PET; nuclear medicine
Dr. Gladys Goh Lo

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Guest Editor
Department of Diagnostic & Interventional Radiology, Hong Kong Sanatorium & Hospital, Happy Valley, Hong Kong SAR, China
Interests: mpMRI; diagnosis

Special Issue Information

Dear Colleagues, 

Advanced imaging has been playing an increasingly important role in the management of prostate cancer (PCa). For instance, multi-parametric MRI (mpRI) defined in The Prostate Imaging-Reporting and Data System (PI-RADS) is a novel promising tool for the diagnosis of prostate cancer that might help to reduce overdiagnosis of insignificant prostate cancer. mpMRI-targeted and PSMA-targeted PET biopsies are both known to have increased the detection rate and diagnostic accuracy of clinically significant prostate cancer. Together, they improve risk stratification and help in the selection of low-risk PC patients for active surveillance. Individually, mpMRI is effective in T staging by assessment of peri-prostatic tumor infiltration while PSMA-PET is useful in the evaluation of lymph node (N-staging) and distant metastasis (M-staging). The emerging utility of integrated PET/MRI is gaining importance as a multi-modality approach in the comprehensive management of PCa.

Besides diagnosis, MRI and PET have also recently contributed to the advancement of therapeutics for PCa, such as in radiation therapy, focal ablative therapy, and surgery. MRI-aided or MRI-only radiotherapy treatment planning has been increasingly adopted in routine clinical practice, while its utilization has yet to be standardized. MRI-guided adaptive radiotherapy (MRgART) empowered by the introduction of MRI-integrated linear accelerators (MR-LINAC) represents an innovative PC treatment technique, which hopefully will further improve treatment outcomes and reduce toxicities. On the other hand, PSMA-PET has gained a definitive role in the management of biochemical recurrence after primary cancer treatment and early identification of oligometastatic disease which is most critical in determining the chance of cure by either salvage radiotherapy or salvage lymph node dissection. Recent advancements in radiomics and artificial intelligence are also facilitating imaging markers in combination with other biomarkers in PCa management.

This Special Issue aims at providing a comprehensive picture of this lively research area by gathering contributions covering all aspects related to the use of advanced imaging in PCa management. We encourage the submissions of both original and review articles, including but not limited to technical, experimental, translational, and clinical studies. 

Dr. Jing Yuan
Dr. Darren M. C. Poon
Dr. Peter Ka-Fung Chiu
Dr. Garrett Chi Lai Ho
Dr. Gladys Goh Lo
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. 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

  • mpMRI
  • PET
  • prostate cancer
  • diagnosis
  • prognosis
  • treatment response
  • precision medicine
  • imaging biomarkers
  • artificial intelligence
  • radiomics

Published Papers (2 papers)

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Research

13 pages, 3466 KiB  
Article
Evaluation of the Reliability and the Performance of Magnetic Resonance Imaging Radiomics in the Presence of Randomly Generated Irrelevant Features for Prostate Cancer
by Cindy Xue, Jing Yuan, Gladys G. Lo, Darren M. C. Poon and Winnie C. W. Chu
Diagnostics 2023, 13(23), 3580; https://doi.org/10.3390/diagnostics13233580 - 01 Dec 2023
Viewed by 792
Abstract
Radiomics has the potential to aid prostate cancer (PC) diagnoses and prediction by analyzing and modeling quantitative features extracted from clinical imaging. However, its reliability has been a concern, possibly due to its high-dimensional nature. This study aims to quantitatively investigate the impact [...] Read more.
Radiomics has the potential to aid prostate cancer (PC) diagnoses and prediction by analyzing and modeling quantitative features extracted from clinical imaging. However, its reliability has been a concern, possibly due to its high-dimensional nature. This study aims to quantitatively investigate the impact of randomly generated irrelevant features on MRI radiomics feature selection, modeling, and performance by progressively adding randomly generated features. Two multiparametric-MRI radiomics PC datasets were used (dataset 1 (n = 260), dataset 2 (n = 100)). The endpoint was to differentiate pathology-confirmed clinically significant (Gleason score (GS) ≥ 7) from insignificant (GS < 7) PC. Random features were generated at 12 levels with a 10% increment from 0% to 100% and an additional 5%. Three feature selection algorithms and two classifiers were used to build the models. The area under the curve and accuracy were used to evaluate the model’s performance. Feature importance was calculated to assess features’ contributions to the models. The metrics of each model were compared using an ANOVA test with a Bonferroni correction. A slight tendency to select more random features with the increasing number of random features introduced to the datasets was observed. However, the performance of the radiomics-built models was not significantly affected, which was partially due to the higher contribution of radiomics features toward the models compared to the random features. These reliability effects also vary among datasets. In conclusion, while the inclusion of additional random features may still slightly impact the performance of the feature selection, it may not have a substantial impact on the MRI radiomics model performance. Full article
(This article belongs to the Special Issue Advanced Imaging in Prostate Cancer Management: Current Status and Future Perspectives)
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17 pages, 4365 KiB  
Article
Joint Cancer Segmentation and PI-RADS Classification on Multiparametric MRI Using MiniSegCaps Network
by Wenting Jiang, Yingying Lin, Varut Vardhanabhuti, Yanzhen Ming and Peng Cao
Diagnostics 2023, 13(4), 615; https://doi.org/10.3390/diagnostics13040615 - 07 Feb 2023
Cited by 2 | Viewed by 1585
Abstract
MRI is the primary imaging approach for diagnosing prostate cancer. Prostate Imaging Reporting and Data System (PI-RADS) on multiparametric MRI (mpMRI) provides fundamental MRI interpretation guidelines but suffers from inter-reader variability. Deep learning networks show great promise in automatic lesion segmentation and classification, [...] Read more.
MRI is the primary imaging approach for diagnosing prostate cancer. Prostate Imaging Reporting and Data System (PI-RADS) on multiparametric MRI (mpMRI) provides fundamental MRI interpretation guidelines but suffers from inter-reader variability. Deep learning networks show great promise in automatic lesion segmentation and classification, which help to ease the burden on radiologists and reduce inter-reader variability. In this study, we proposed a novel multi-branch network, MiniSegCaps, for prostate cancer segmentation and PI-RADS classification on mpMRI. MiniSeg branch outputted the segmentation in conjunction with PI-RADS prediction, guided by the attention map from the CapsuleNet. CapsuleNet branch exploited the relative spatial information of prostate cancer to anatomical structures, such as the zonal location of the lesion, which also reduced the sample size requirement in training due to its equivariance properties. In addition, a gated recurrent unit (GRU) is adopted to exploit spatial knowledge across slices, improving through-plane consistency. Based on the clinical reports, we established a prostate mpMRI database from 462 patients paired with radiologically estimated annotations. MiniSegCaps was trained and evaluated with fivefold cross-validation. On 93 testing cases, our model achieved a 0.712 dice coefficient on lesion segmentation, 89.18% accuracy, and 92.52% sensitivity on PI-RADS classification (PI-RADS ≥ 4) in patient-level evaluation, significantly outperforming existing methods. In addition, a graphical user interface (GUI) integrated into the clinical workflow can automatically produce diagnosis reports based on the results from MiniSegCaps. Full article
(This article belongs to the Special Issue Advanced Imaging in Prostate Cancer Management: Current Status and Future Perspectives)
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