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

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 communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

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.