> For the complete documentation index, see [llms.txt](https://medomicslab.gitbook.io/mediml-app-docs/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://medomicslab.gitbook.io/mediml-app-docs/radiomics.md). # Radiomics ### What is radiomics? Radiomics refers to the high-throughput quantitative image analysis which aims at extracting a large number of features characterizing a region of interest in the medical image. Radiomics analysis can be performed on medical images from different modalities, e.g., from from magnetic resonance imaging (MRI), computed tomography (CT), and positron-emission-tomography (PET). This approach enhances the existing data available to clinicians by quantifying the spatial distribution of signal intensities, voxels interrelationships and textural information.

Image credit: Review on Radiomics for Brain Tumors.

### Radiomics typical workflow The figure below demonstrates the necessary stages in a typical radiomics pipeline, emphasizing the crucial aspects of the radiomics analysis procedure.

Radiomics analysis typical workflow

### Radiomics features Feature extraction is the final processing step where feature descriptors are utilized to quantify characteristics of the grey levels within the ROI. The set of features can be classified into various families, including intensity-based statistical features, intensity histogram-based features, intensity-volume histogram-based features, morphological features, local intensity features, and texture matrix-based features. Each family has the potential to reveal tumoral patterns, size, shape, and textural characteristics that may not be discernible to the naked eye. Additionally, features can be computed on the base image, as well as from filtered images.

Feature categories

Many different ways and formulas exist to calculate these features, therefore, adherence to the [Image Biomarker Standardization Initiative (IBSI)](https://theibsi.github.io/) guidelines is recommended. These guidelines offer a consensus for standardized feature calculations from all radiomic features. **Craving more details? Keep reading** [**here**](https://medium.com/@mahdiall99/radiomics-in-medical-imaging-a-brief-introduction-3208863aae87)**.** --- # Agent Instructions This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com. ## Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter, and the optional `goal` query parameter: ``` GET https://medomicslab.gitbook.io/mediml-app-docs/radiomics.md?ask=&goal= ``` `ask` is the immediate question: it should be specific, self-contained, and written in natural language. `goal` is optional and describes the broader end goal you are ultimately trying to accomplish on behalf of the user. GitBook uses it to tailor the answer towards what is most useful for that goal. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.