Radiomics

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.

 

HealthMyne, a pioneer in applied radiomics, announced today that peer-reviewed research recently published in the journal Cancers has demonstrated the ability of its radiomics technology to identify biomarkers that predict whether patients with lung adenocarcinoma would benefit from immunotherapy.

Semi-automatic lesion identification: (A) Manual ROI indication. In blue, it is possible to observe the axes that cross the lesion manually delineated by the radiologist on a plane of the MPR. The intensity of the lesion boundary (estimated) is represented with a red outline. (B) Additional axes can be dragged onto other orthogonal MPR views. From left to right, it is possible to observe the initial long axis outlined by the radiologist and the 2D contours on the axial, coronal and sagittal views of the lesion used as a starting point for the RPM algorithms. (C) Resulting 3D contour of the lesion (in blue).

News | Radiomics | September 21, 2021
September 21, 2021 —  HealthMyne, a pioneer in applied radiomics, announced today that peer-reviewed research recently...
AIMed conference in artificial intelligence in radiology, medical imaging.
Feature | Artificial Intelligence | May 13, 2019
The integration of artificial intelligence (AI) into medicine has by far been the hottest topic at nearly all medical...
Researchers Use Radiomics to Predict Who Will Benefit from Chemotherapy
News | Radiomics | March 21, 2019
March 21, 2019 — Using data from computed tomography (CT) images, researchers may be able to predict which lung cancer...
Novel Technique May Significantly Reduce Breast Biopsies
News | Breast Biopsy Systems | January 17, 2019
January 17, 2019 — A novel technique that uses mammography to determine the biological tissue composition of a tumor...
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
I attended the American Association of Physicists in Medicine (AAPM) 2018 meeting in late July and came back with many...
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 magnetic resonance images (MRIs) can help identify people with attention...
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 Case...
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 radiation therapy...