Magnetic resonance imaging radiomics in prostate cancer radiology: what is currently known?

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Abstract

Diagnostic and treatment approaches in prostate cancer rely on a combination of magnetic resonance imaging and histological data.

This study aimed to introduce the basics of the current diagnostic approach in prostate cancer with a focus on texture analysis.

Texture analysis evaluates the relationships between image pixels using mathematical methods, which provide additional information. First-order texture analysis of features can have greater clinical reproducibility than higher-order texture features. Textural features that are extracted from diffusion coefficient maps have shown the greatest clinical relevance. Future research should focus on integrating machine learning methods to facilitate the use of texture analysis in clinical practice.

The development of automated segmentation methods is required to reduce the likelihood of including normal tissue in the area of interest. Texture analysis allows the noninvasive separation of patients into groups in terms of possible treatment options. Currently, few clinical studies reported on the differential diagnosis of clinically significant prostate cancer, including the Gleason and International Society of Urological Pathology grading. Large prospective studies are required to verify the diagnostic potential of textural features.

About the authors

Pavel B. Gelezhe

Moscow Center for Diagnostics and Telemedicine; European Medical Center

Email: gelezhe.pavel@gmail.com
ORCID iD: 0000-0003-1072-2202
SPIN-code: 4841-3234

MD, Cand. Sci. (Med.)

Russian Federation, 24-1 Petrovka street, 127051, Moscow; Moscow

Ivan A. Blokhin

Moscow Center for Diagnostics and Telemedicine

Email: i.blokhin@npcmr.ru
ORCID iD: 0000-0002-2681-9378
SPIN-code: 3306-1387
Russian Federation, 24-1 Petrovka street, 127051, Moscow

Serafim S. Semenov

Moscow Center for Diagnostics and Telemedicine; Moscow Clinical Scientific Center named after A.S. Loginov

Email: s.semenov@npcmr.ru
ORCID iD: 0000-0003-2585-0864
SPIN-code: 4790-0416

MD

Russian Federation, 24-1 Petrovka street, 127051, Moscow; Moscow

Damiano Caruso

Sapienza University of Rome; Sant’Andrea University Hospital

Author for correspondence.
Email: dcaruso85@gmail.com
ORCID iD: 0000-0001-9285-4764

MD, PhD, Department of Surgical and Medical Sciences and Translational Medicine, Radiology Unit

Italy, Rome; Rome

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Supplementary files

Supplementary Files
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2. Fig. 1. A model of the radiomics workflow based on T2-weighted images in prostate cancer.

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3. Fig. 2. Segmentation and evaluation of entropy of the tumor focus of the transitional zone of the prostate gland.

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4. Fig. 1. Radiomics workflow model based on T2-weighted images in prostate cancer

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5. Fig. 2. Segmentation and evaluation of the entropy of the tumor focus of the transition zone of the prostate

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Copyright (c) 2022 Gelezhe P.B., Blokhin I.A., Semenov S.S., Caruso D.

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This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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