We use cookies to understand how you use our site and to improve your experience. This includes personalizing content and advertising. To learn more, click here. By continuing to use our site, you accept our use of cookies. Cookie Policy.

Features Partner Sites Information LinkXpress
Sign In
Advertise with Us
Ampronix,  Inc

Download Mobile App


ATTENTION: Due to the COVID-19 PANDEMIC, many events are being rescheduled for a later date, converted into virtual venues, or altogether cancelled. Please check with the event organizer or website prior to planning for any forthcoming event.

Single Chest X-Ray Predicts Mortality Risk

By MedImaging International staff writers
Posted on 08 Aug 2019
Print article
Image: A new study claims that chest x-rays cam predict longevity (Photo courtesy of Getty Images).
Image: A new study claims that chest x-rays cam predict longevity (Photo courtesy of Getty Images).
A new study suggests that a convolutional neural network (CNN) can stratify all-cause mortality risk based on a single chest radiograph.

Developed at Massachusetts General Hospital (MGH; Boston, USA), Harvard Medical School (HMS; Boston, MA, USA), and other institutions, the CNN algorithm, named CXR-risk, uses data from radiologists' diagnostic findings (such as presence of a lung nodule) on a chest x-ray, and combines it with other risk factors, including age, sex and comorbidities in order to predict long-term mortality, including non-cancer death. A deep learning CXR-risk score (very low, low, moderate, high, and very high) is generated based on CNN analysis of a submitted radiograph.

To develop the CNN, the researchers used 41,856 x-rays from the screening radiography arm of the Prostate, Lung, Colorectal, and Ovarian (PLCO) cancer screening trial, a community cohort of asymptomatic nonsmokers and smokers enrolled at 10 U.S. sites from November 8, 1993, through July 2, 2001. The results of the CNN were tested in a further 10,464 cases from the screening radiography arm of the National Lung Screening Trial (NLST), a community cohort of heavy smokers enrolled at 21 U.S. sites from August 2002 through April 2004.

The results revealed a graded association between CXR-risk score and mortality. The very high-risk group had an all-cause mortality of 53% (PLCO) and 33.9% (NLST), compared with the very low-risk group. The association was robust to adjustment for radiologists’ findings and risk factors. Comparable results were seen for lung cancer death, non-cancer cardiovascular death, and respiratory death. The study was published on July 19, 2019, in JAMA Network Open.

“We get chest x-rays to make a diagnosis like pneumonia, but our study shows that there is also free prognostic information about health and longevity on the images. Based on the chest x-ray image alone, AI identified people at up to a 53% risk of death over 12 years,” said lead author Michael Lu, MD, MPH, of MGH and HMS. “Scores calculated using AI may incentivize high-risk individuals to lower their chance of dying with prevention, regular screening, and lifestyle modification.”

Deep learning is part of a broader family of AI machine learning methods based on learning data representations, as opposed to task specific algorithms. It involves CNN algorithms that use a cascade of many layers of nonlinear processing units for feature extraction, conversion and transformation, with each successive layer using the output from the previous layer as input to form a hierarchical representation.

Related Links:
Massachusetts General Hospital
Harvard Medical School

Print article


Copyright © 2000-2020 Globetech Media. All rights reserved.