Radiogenomics and Radiomics in the Diagnostics  of Malignant Tumours: A Literary Review

Огнерубов, Н.А.; Shatov, I.A.; Shatov, A.V.

doi:10.20310/1810-0198-2017-22-6-1453-1460

Cited by 10 publications

(6 citation statements)

References 0 publications

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“…В последние годы радиомика всё чаще применяется для диагностики, прогнозирования ответа на лечение и отдалённых исходов опухолей нервной системы [26,66,67], головы и шеи [68,69], желудочно-кишечного тракта [70,71], рака простаты [72,73] Funding source. The authors declare that there is no external funding for the exploration and analysis work.…”

Section: роль разработки биомаркеров визуализацииunclassified

The role of the quality control system for diagnostics of oncological diseases in radiomics: a review

Khoruzhaya

Николаевна²,

Ахмад

et al. 2021

Digital Diagnostics

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Modern medical imaging methods allow for both qualitative and quantitative evaluations of tumors and issues surrounding them. Advances in computer science and big data processing are transforming any radiological study into analytic datasets, especially with the use of machine learning in medical image analysis. Among these datasets, statistically significant correlations with clinical events can then be searched for to subsequently assess their predictive value and ability to predict a particular clinical outcome. This concept, known as radiomics, was first described in 2012. It is particularly important in oncology because each type of tumor can be subdivided into many different molecular genetic subtypes, and simple visual characteristics are no longer sufficient. Moreover, as an absolutely noninvasive method, radiomics can provide a radiologist with additional information that would otherwise be unavailable without a histological examination of biopsy material. However, as with any methodology based on the use of big data, the question of the quality of the initial data becomes critical, because this can directly affect the outcome of the analysis and provide incorrect diagnostic information. In this literature review, we examine potential approaches to ensuring the quality of research at all stages, from technical control of the state of diagnostic equipment to the extraction of imaging markers in oncology and the calculation of their correlation with clinical data.

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Section: роль разработки биомаркеров визуализацииunclassified

The role of the quality control system for diagnostics of oncological diseases in radiomics: a review

Khoruzhaya

Николаевна²,

Ахмад

et al. 2021

Digital Diagnostics

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“…Радиомика -процесс, состоящий из этапов подготовки и последующего количественного анализа многомерных данных, полученных из цифровых медицинских изображений (суффикс «омик» присутствует в названиях направлений молекулярной биологии, специализирующихся на больших объёмах данных [24]). Радиомика описывается как анализ изображений с использованием определённых алгоритмов, направленных на извлечение числовых характеристик изображений, с целью создания моделей классификации для усовершенствования поддержки принятия медицинских решений, а также для определения прогноза заболевания [25,26] и лечения [27], что особенно ценно для персонализированной терапии. В радиомике одна область интереса изображения используется для получения множества (иногда десятков и сотен) числовых характеристик, каждое из которых может нести определённый теоретико-информационный аспект (часто называемый «радиомическим признаком»), который недоступен при обычном просмотре изображений [15].…”

Section: радиомика и радиогеномикаunclassified

“…Радиогеномика -технология, устанавливающая связь между генотипом пациента и фенотипом визуализации. Необходимо отметить, что термин «радиогеномика» может также употребляться по отношению к генетической изменчивости и её связи с ответом на лучевую терапию [29,30], но чаще используется при оценке взаимосвязи между характеристиками изображения опухоли или какого-то заболевания с его образцами экспрессии генов и мутациями генов [25,26].…”

Section: радиомика и радиогеномикаunclassified

“…Извлечение множества радиомических признаков из сегментированной области интереса с помощью математических операций с цифровыми значениями интенсивностей и взаиморасположения пикселей или вокселей на изображениях. Извлечённые количественные признаки подразделяются на категории: морфологические (объём и форма), гистограммные признаки (описание интенсивности уровней серого тона) первого, второго и более высоких порядков [26,34].…”

Section: этапы радиомикиunclassified

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The role of mammography in breast cancer radiomics

Govorukhina

Георгиевна²,

Semenov

et al. 2021

Digital Diagnostics

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Mammography is still the only screening method for breast cancer. Although digital mammography is the most common and widely used method for detecting breast cancer, it is ineffective at detecting and assessing intratumoral heterogeneity. Due to the small size of the tissue sample or tumor, biopsies often fail to represent the entire tumor. For this reason, selecting a treatment and determining a patients prognosis becomes difficult. In this case, medical imaging is a noninvasive approach that can provide a more comprehensive view of the entire tumor, act as a virtual biopsy, and be useful for monitoring disease progression and response to therapy. Radiomics with texture analysis allows you to look at an image as a group of numerical data, moving beyond the usual visual perception and into a deeper analysis of digital, pixel data to improve the accuracy of differential diagnosis. Radiogenomics is a natural extension of radiomics that focuses on determining gene expression based on radiologic tumor phenotype. The purpose of this review is to evaluate the role of mammography in breast cancer radiomics and radiogenomics. The article presents a literature review of relevant Russian scientific articles found in databases such as PubMed, Medline, Springer, eLibrary, and Google Scholar. The information obtained was then pooled, structured, and analyzed to examine the role of mammography in breast cancer screening radiomics.

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“…Digital images of the tumor obtained with their aid reflect its anatomical and functional changes. However, most of these data are largely nonspecific and insufficiently informative [1][2][3].…”

Section: Introductionmentioning

confidence: 99%

Radiomics and Digital Image Texture Analysis in Oncology (Review)

Litvin¹,

Burkin²,

Kropinov³

et al. 2021

Sovrem Tehnol Med

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One of the most promising areas of diagnosis and prognosis of diseases is radiomics, a science combining radiology, mathematical modeling, and deep machine learning. The main concept of radiomics is image biomarkers (IBMs), the parameters characterizing various pathological changes and calculated based on the analysis of digital image texture. IBMs are used for quantitative assessment of digital imaging results (CT, MRI, ultrasound, PET). The use of IBMs in the form of "virtual biopsy" is of particular relevance in oncology.The article provides the basic concepts of radiomics identifying the main stages of obtaining IBMs: data collection and preprocessing, tumor segmentation, data detection and extraction, modeling, statistical processing, and data validation. The authors have analyzed the possibilities of using IBMs in oncology, describing the currently known features and advantages of using radiomics and image texture analysis in the diagnosis and prognosis of cancer. The limitations and problems associated with the use of radiomics data are considered.Although the novel effective tool for performing virtual biopsy of human tissue is at the development stage, quite a few projects have already been implemented, and medical software packages for radiomics analysis of digital images have been created.

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Radiogenomics and Radiomics in the Diagnostics of Malignant Tumours: A Literary Review

Cited by 10 publications

References 0 publications

The role of the quality control system for diagnostics of oncological diseases in radiomics: a review

The role of the quality control system for diagnostics of oncological diseases in radiomics: a review

The role of mammography in breast cancer radiomics

Radiomics and Digital Image Texture Analysis in Oncology (Review)

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