News | Artificial Intelligence | March 15, 2018

Median Technologies and the Nice University Hospital to Use AI in Lung Cancer Screening

The collaboration will use Deep Learning techniques to establish medical imaging biomarkers for more accurate diagnosis

 

Median Technologies and the Nice University Hospital to Use AI in Lung Cancer Screening

March 15, 2018 – Median Technologies, the industry-leading Imaging Phenomics Company and the Nice University Hospital (CHU de Nice) today announced a collaborative agreement that uses Artificial Intelligence to identify medical imaging biomarkers for lung cancer screening. These efforts will enable more accurate diagnosis and provide physicians with new therapeutic decision-making tools, based on medical imaging.

As part of the collaboration, medical imaging data from the AIR study - a French, multicenter cohort study, led by the Nice Hospital that has enrolled, to-date, more than 600 high-risk patients (smokers or former smokers with Chronic Obstructive Pulmonary Disease [COPD]) screened for lung cancer - will be analyzed to identify and characterize pulmonary nodules visible in thoracic CT scans. By using Deep Learning methods, a discipline of Artificial Intelligence, Median will develop new algorithms to identify imaging biomarkers that indicate pulmonary nodule malignity.

While current CT scan performance enables more pulmonary abnormalities to be identified, post-treatment image applications do not allow for an automatic, accurate characterization of the malignity or benignity of these pulmonary abnormalities. Lung nodule biopsies, which are invasive, are needed to confirm a diagnosis - potentially leading to complications for patients. By using medical imaging biomarkers, clinicians can reduce unnecessary biopsies and more accurately diagnose patients.

"Early detection of lung cancer is of paramount importance if we want to lessen mortality of this disease", says Professor Charles Marquette, coordinator of clinical teams in the AIR study. "The rationale for screening is based on the tight relationship between outcome and extent of the disease at time of diagnosis. However, large-scale screening of unselected population with chest computed tomography (CT) is expensive and has a high harm to benefit ratio, which explains why many health agencies are reluctant to implement screening of lung cancer with chest CT alone. We are developing a multimodal approach to lung cancer screening, including refinement of screening criteria (e.g. focus on COPD), non-invasive biomarkers and use of Artificial Intelligence to better characterize chest CT findings.”

"Today, many pulmonary biopsies are performed unnecessarily; Artificial Intelligence is going to make imaging, which represents non-invasive and less expensive procedures, an improved therapeutic decision-making tool. With Artificial Intelligence, imaging will help to identify patients who really need a biopsy and will contribute to advance clinical practice," said Peter Bannister, Chief Technology Officer at Median Technologies.

Related Content

HeartFlow Analysis Successfully Stratifies Heart Disease Patients at One Year
News | CT Angiography (CTA) | March 19, 2019
Late-breaking results confirm the HeartFlow FFRct (fractional flow reserve computed tomography) Analysis enables...
Jennifer N. A. Silva, M.D., a pediatric cardiologist at Washington University School of Medicine in Saint Louis, Mo., describes “mixed reality” at ACC19 Future Hub.

Jennifer N. A. Silva, M.D., a pediatric cardiologist at Washington University School of Medicine in Saint Louis, Mo., describes “mixed reality” at ACC19 Future Hub.

Feature | Cardiac Imaging | March 17, 2019 | By Greg Freiherr
Virtual reality (VR) and its less immersive kin, augmented reality (AR), are gaining traction in some medical applica
WVU cardiology chief Partho Sengupta, M.D., describes at ACC 2019 how artificial intelligence already helps cardiologists in echocardiography.

WVU cardiology chief Partho Sengupta, M.D., describes at ACC 2019 how artificial intelligence already helps cardiologists in echocardiography. Photo by Greg Freiherr

Feature | Cardiac Imaging | March 16, 2019 | By Greg Freiherr
Machine learning is already having an enormous impact on cardiology, automatically calculating measurements in echoca
Bay Labs Announces New Data on EchoGPS, AutoEF AI Software at ACC.19
News | Cardiovascular Ultrasound | March 15, 2019
Artificial intelligence (AI) company Bay Labs announced the presentation of two studies assessing performance of the...
Podcast | Cardiac Imaging | March 15, 2019
Debate About Coronary Testing Highlights ACC Session
Canon Medical Introduces Entry-Level Aquilion Start CT
News | Computed Tomography (CT) | March 14, 2019
Canon Medical Systems Europe B.V. introduced the all-new Aquilion Start computed tomography (CT) system to the European...
Siemens Healthineers Debuts Cardiovascular Edition of Somatom go.Top CT
News | Computed Tomography (CT) | March 14, 2019
Siemens Healthineers will introduce the Somatom go.Top Cardiovascular Edition, a new version of its established...
CT, Mammograms Offer Clues to Preventing Heart Problems After Cancer Treatment
News | Cardio-oncology | March 13, 2019
An imaging procedure commonly performed before starting cancer treatment can provide valuable clues about a patient's...
Sponsored Content | Videos | Artificial Intelligence | March 13, 2019
At RSNA 2018, iCad showed how its...
Lucence Diagnostics to Develop AI Tools for Liver Cancer Treatment

Pseudocolor accentuated CT scan image of a liver tumor. Image courtesy of Lucence Diagnostics.

News | Oncology Diagnostics | March 12, 2019
Genomic medicine company Lucence Diagnostics announced a new project to develop artificial intelligence (AI) algorithms...