top of page

KLINIKUM | What is Radiomics & why does it matter?

What is Radiomics?

Radiomics is a relatively new field in medical imaging that uses quantitative analysis of medical images to extract features that can be used to better understand and diagnose diseases. These features, called radiomic features, can be used to characterize the morphology, texture, and other aspects of a tumor or other abnormality.

Radiomics is still a developing field, but it has the potential to revolutionize the way we diagnose and treat diseases. By extracting and analyzing these radiomic features, radiologists can gain a deeper understanding of the underlying biology of a disease, which can lead to more accurate diagnoses and better treatment outcomes.

How is Radiomics Significant in Today's Healthcare Industry?

Radiomics is significant in today's healthcare industry for a number of reasons. First, it can be used to improve the accuracy of cancer diagnosis. By extracting radiomic features from tumor images, radiologists can better characterize the tumor and identify those that are more likely to be aggressive. This can help to ensure that patients receive the correct treatment at the earliest possible stage.

Second, radiomics can be used to predict patient outcomes. By tracking radiomic features over time, radiologists can better understand how a tumor is responding to treatment. This information can be used to adjust treatment plans as needed, which can improve patient outcomes.

Third, radiomics can be used to develop new biomarkers. Biomarkers are biological indicators that can be used to track the progression of a disease or to predict patient response to treatment. Radiomics can be used to identify new biomarkers that are more accurate and reliable than traditional biomarkers.

🩻 TECH SCOOP: Common Radiomics Features

​Intensity-Based Features

Shape-Based Features

Texture-Based Features

Transform-Based Features

​Features that are based on the intensity values of the voxels in the image

Features that describe the shape of the object in the image

Features that describe the spatial distribution of intensity values in the image

​Features that are derived from applying mathematical transforms to the image

🔎 Mean

🔎 Compactness


🔎 Wavelength

🔎 Standard Deviation

🔎 Roundness

🔎 RLM*

🔎 Fourier

🔎 Median

🔎 Sphericity

🔎 SZM*

🔎 Fractal

🔎 Skewness

🔎 Elongation


🔎 Kurtosis

🔎 Fractal dimension


  • Gray-level co-occurrence matrix (GLCM)

  • Run length matrix (RLM)

  • Size zone matrix (SZM)

  • Neighbourhood gray tone difference matrix (NGTDM)

These are just a few of the many radiomics features that can be extracted from medical images. The specific features that are used will depend on the type of image, the disease being studied, and the goals of the study/case.

The Future of Radiomics

Radiomics is a rapidly evolving field, and its potential applications are vast. In the future, radiomics is likely to be used to improve the diagnosis, treatment, and monitoring of a wide range of diseases. It is also likely to be used to develop new personalized treatments that are tailored to the individual patient.

The promise land of #precisionmedicine is here!

Here are some additional resources about radiomics:

Radiomics: A Critical Step towards Integrated Healthcare:

Radiomics: 5 Things You Need to Know:

Radiomics and Machine Learning in Oral Healthcare:

The Role of Radiomics and AI Technologies in the Segmentation:


One step further? Armor thy mind with knowledge 👩🏿‍💻


bottom of page