Radiomics capabilities in the interpretation of ultrasound and CT data in patients with chronic kidney disease: A review

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Abstract

The purpose of this review is to explore the possibilities of radiomics in interpreting ultrasound and multislice spiral computed tomography data in patients with chronic kidney disease (CKD). Radiomics is a promising area of medical image analysis based on the extraction of quantitative features not available in standard visual analysis and the subsequent use of artificial intelligence methods for their processing and interpretation. The article discusses the basics of radiomic methods, including texture analysis of images and the creation of diagnostic models using machine learning algorithms. The advantages of radiomic characteristics, in particular statistical features of order II and higher orders, in assessing interstitial fibrosis and other abnormal changes in the renal parenchyma are discussed in detail. The results of studies demonstrating a strong correlation of radiomic signs with histological changes detected during kidney biopsy are presented. The prospects of radiomics as a non-invasive approach for assessing kidney damage and monitoring CKD progression are emphasized. The conclusion indicates the need for further research to standardize and expand the use of radiomic methods in clinical practice to improve the diagnosis accuracy and prognostic assessment of patients with CKD.

About the authors

Alexandra V. Proskura

Sechenov First Moscow State Medical University (Sechenov University)

Author for correspondence.
Email: proskura_a_v_1@staff.sechenov.ru
ORCID iD: 0000-0003-0441-4799

канд. мед. наук, врач-уролог, онколог, ассистент Института урологии и репродуктивного здоровья человека 

Russian Federation, Moscow

Khalil M. Ismailov

Sechenov First Moscow State Medical University (Sechenov University)

Email: proskura_a_v_1@staff.sechenov.ru
ORCID iD: 0000-0003-0548-190X

врач-уролог, аспирант Института урологии и репродуктивного здоровья человека 

Russian Federation, Moscow

Alexander G. Smoleevskiy

Sechenov First Moscow State Medical University (Sechenov University)

Email: proskura_a_v_1@staff.sechenov.ru
ORCID iD: 0000-0002-8771-8589

ординатор Института урологии и репродуктивного здоровья человека 

Russian Federation, Moscow

Amina I. Salpagarova

Sechenov First Moscow State Medical University (Sechenov University)

Email: proskura_a_v_1@staff.sechenov.ru
ORCID iD: 0009-0006-9642-7202

студентка IV курса Института клинической медицины им. Н.В. Склифосовского 

Russian Federation, Moscow

Irina N. Bobkova

Sechenov First Moscow State Medical University (Sechenov University)

Email: proskura_a_v_1@staff.sechenov.ru
ORCID iD: 0000-0002-8007-5680

д-р мед. наук, проф. каф. внутренних и профессиональных болезней и ревматологии Института клинической медицины им. Н.В. Склифосовского 

Russian Federation, Moscow

Andrei M. Shestiuk

Brest Regional Clinical Hospital

Email: proskura_a_v_1@staff.sechenov.ru
ORCID iD: 0000-0002-2624-5773

канд. мед. наук, доц., зав. отд-нием трансплантологии 

Belarus, Brest

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2. Fig. 1. The process of creating a test model in radiomics [1].

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3. Fig. 2. Illustration comparing the possibilities of statistics of the I and II orders [2].

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4. Fig. 3. Voxel map of high-order textural features [22].

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