Radiomics is the study of information hidden in imaging exams that machine algorithms are trained to identify to help doctors more accurately diagnose patients, stage cancers, determine optimum therapies, predict patient outcomes or their risk level choose the radiation therapy dose level of risk. The field of medical study extracts large amounts of quantitative features from medical images using data characterization algorithms. These features, called radiomic features, may be able to uncover disease characteristics that fail to be appreciated by the naked eye. It is expected this field will be dominated by artificial intelligence software in the coming years.


Videos | Radiation Oncology | November 06, 2018

Genomics can be used to assess a patient's radiosensitivity, which can be used to increase or decrease the radiation...

Cherenkov radiation imaging during radiation therapy

Cherenkov radiation imaging during radiation therapy

Blog | AAPM | August 14, 2018
Videos | Radiomics | August 09, 2018

A discussion with Martin Vallieres, Ph.D., post-doctoral fellow at McGill University, Montreal, Canada. He spoke on...

Illuminate and Medexprim Partner to Enhance PACS Data Mining
News | PACS | May 30, 2018

May 30, 2018 — U.S.-based Softek Illuminate and the entrepreneurial French firm Medexprim will be combining,...

Researchers Use Radiomics to Overcome False Positives in Lung Cancer CT Screening
News | Advanced Visualization | May 29, 2018

May 29, 2018 — A team of researchers including investigators from Mayo Clinic has identified a technology to address...

MRI Shows Brain Differences Among ADHD Patients
News | Neuro Imaging | January 02, 2018

January 2, 2018 — Information from brain...

Case Western Reserve University study, machine learning, MRI, brain cancer diagnoses, radiomics

MRI scans of patients with radiation necrosis (above) and cancer recurrence (below) are shown in the left column. Close-ups in the center column show the regions are indistinguishable on routine scans. Radiomic descriptors unearth subtle differences showing radiation necrosis, in the upper right panel, has less heterogeneity, shown in blue, compared to cancer recurrence, in the lower right, which has a much higher degree of heterogeneity, shown in red. Credit: Pallavi Tiwari

News | Analytics Software | October 25, 2016

October 25, 2016 — Computer programs have defeated humans in Jeopardy!, chess and Go. Now a program developed at...

Radiomics uses computer algorithms to pull out data from imaging to use in automated diagnosis and risk stratification, which otherwise is not evident to the human eye.Dave Fornell
News | July 30, 2014

July 30, 2014 — Information hidden in imaging tests could help doctors more accurately choose the...