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The findings were true regardless of tumor type, size or cancer stage, wrote Pragya A. Dang, MD, and colleagues at Brigham and Women’s Hospital in Boston.

Results from a study published Oct. 14 in Nature Communications showed the tracer could identify pancreatic, cervical and lung cancer, in addition to a lung tissue disease called idiopathic pulmonary fibrosis.

On Jan. 1, 2020, the Protecting Access to Medicare Act of 2014 will require that physicians consult appropriate use criteria for ordering advanced imaging studies. Radiologists can help make sure clinical decision support tools help, not hurt imaging decisions.

Men make up nearly 1% of all breast cancer cases in the U.S., but their mortality rate is drastically higher compared to women diagnosed with the disease.

Santa Clara, California-based NVIDIA and King's College London are teaming up to create a new federated learning system to advance medical imaging research.

Images of patients with traumatic head injuries revealed that microbleeds appear in the form of small, dark lesions and may predict worse outcomes, according to a new study published in Brain.

Researchers found measurements performed with their full-scale airway network flow model based on CT imaging data compared similarly to measurements derived from functional lung imaging. In addition to improving COPD analysis, the platform can help shed light on many forms of lung disease.

Deep learning offers similar detection of prostate cancer on MRI compared to prostate imaging reporting and data system (PI-RADS) assessments, according to new research out of Germany.

Differences in brain circuitry may indicate an individual’s risk for suicide, according to a recent fMRI-based study published in Psychological Medicine.

The CDC released interim guidance for clinicians to help with the evaluation and management of e-cigarette or vaping product use-associated lung injury, or EVALI.

“At the end of five years, we hope to have a radioactive tracer that will be able to detect Parkinson’s early on and provide detailed information about the disease’s progression, which is critical for discovering and testing new treatments," said Robert H. Mach, PhD, a researcher involved in the project.

“The Center for Intelligent Imaging will serve as a hub for the multidisciplinary development of AI in imaging to meet unmet clinical needs and provide a platform to measure impact and outcomes of this technology,” said Christopher Hess, MD, PhD, chair of the UCSF Department of Radiology and Biomedical Imaging.