Radiomic data has the potential to uncover disease characteristics that fail to be appreciated by the naked eye. The central hypothesis of radiomics is that distinctive imaging algorithms quantify the state of diseases, and thereby provide valuable information for personalized medicine. Radiomics has emerged from oncology, but can be applied to other medical problems where a disease is imaged.

RADIOMIC Technologies
Engineered Features

Engineered features are hard-coded features which are often based on expert domain knowledge. Measures include intensity, shape, texture, wavelet, and LOG features, and have been found useful in several clinical areas, such as oncology and cardiology.

deep learning

Deep learning methods can learn feature representations automatically from data. These features are included in neural nets’ hidden layers. Within radiomics, deep learning involves utilizing convolutional neural nets - or convnets - for building predictive or prognostic non-invasive biomarkers.

Clinical Significance

Scientific studies have assessed the clinical relevance of radiomic features in multiple independent cohorts consisting of lung and head-and-neck cancer patients.

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Predictive Power

Combined with appropriate feature selection and classification methods, radiomic features were examined in terms of their performance and stability for predicting prognosis.

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Multiple open-source platforms have been developed for the extraction of Radiomics features from 2D and 3D images and binary masks and are under continuous development.

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Why Radiomics?

Radiomics studies continue to improve prognosis and theraputic response prediction paving the way for imaging-based precision medicine.

Radiomics on pubmed
Radiomics on google scholar


pubmed papers


google scholar papers is a platform for everything radiomics.

Establish a reference radiomic standard.

Develop and maintain open-source projects.

Provide a practical go-to resource for radiomic applications.

Support radiomic outreach within the science community.