Tổng số lượt xem trang

Thứ Hai, 11 tháng 1, 2021

Radiomic analysis là gì


Dr Henry Knipe and Dr Muhammad Idris et al. From Radiopaedia

Radiomics (as applied to radiology) is a field of medical study that aims to extract a large number of quantitative features from medical images using data characterization algorithms. The data is assessed for improved decision support. It has the potential to uncover disease characteristics that are difficult to identify by human vision alone.


The process of creating a database of correlative quantitative features, which can be used to analyze subsequent (unknown) cases includes the following steps 3.

Initial image processing

Using a variety of reconstruction algorithms such as contrast, edge enhancement, etc. This influences the quality and usability of the images, which in turn determines how easily and accurately an abnormal characteristic could be detected and characterized.

Image segmentation

Identify/create areas (2D images) or volumes of interest (3D images). Can be done either manually, semi-automated, or fully automated using artificial intelligence.

For large data sets, an automated process is needed because manual techniques are usually very time-consuming and tend to be less accurate, less reproducible and less consistent compared with automated artificial intelligence techniques.

Features extraction and qualification

Features include volume, shape, surface, density, and intensity, texture, location, and relations with the surrounding tissues.

Semantic features are those that are commonly used in the radiology lexicon to describe regions of interest.

Agnostic features are those that attempt to capture lesion heterogeneity through quantitative mathematical descriptors.

Examples of semantic features

·         shape

·         location

·         vascularity

·         spiculation

·         necrosis

·         attachments

Equivalent examples of agnostic features

·         histogram (skewness, kurtosis)

·         Haralick textures

·         Laws textures

·         wavelets

·         Laplacian transforms

·         Minkowski functions

·         fractal dimensions


Radiomics can be applied to most imaging modalities including radiographs, ultrasound, CT, MRI and PET studies. It can be used to increase the precision in the diagnosis, assessment of prognosis, and prediction of therapy response, particularly in combination with clinical, biochemical, and genetic data. The technique has been used in oncological studies, but potentially can be applied to any disease.

A typical example of radiomics is using texture analysis to correlate molecular and histological features of diffuse high-grade gliomas 2

The determination of most discriminatory radiomics feature extraction methods varies with the modality of imaging and the pathology studied and is therefore currently (c.2019) the focus of research in the field of radiomics.

Current challenges include the development of a common nomenclature, image data sharing, large computing power and storage requirements, and validating models across different imaging platforms and patient populations.


Promoted articles (advertising)

1.    MAPS: A Quantitative Radiomics Approach for Prostate Cancer Detection

EMBS Trans Biomed Eng, 2015

2.    Robust Collaborative Clustering of Subjects and Radiomic Features for Cancer Prognosis

EMBS Trans Biomed Eng, 2020

3.    Pattern Classification for Gastrointestinal Stromal Tumors by Integration of Radiomics and Deep Convolutional Features

EMBS J Biomed Health Inform, 2018

1.    Multi-Objective-Based Radiomic Feature Selection for Lesion Malignancy Classification

EMBS J Biomed Health Inform, 2019

2.    Benefits and limitations of real-world evidence: lessons from EGFR mutation-positive NSCLC

Bassel Nazha et al., Future Oncology, 2020

3.    A Radiomics Approach With CNN for Shear-Wave Elastography Breast Tumor Classification

EMBS Trans Biomed Eng, 2018




Không có nhận xét nào :