Clinical Roundup: The April Radiology News You Shouldn’t Miss

ACP Updates Guidance on Mammography Screening

In a new guidance statement published in the Annals of Internal Medicine, the American College of Physicians (ACP) now suggests women with average risk of developing breast cancer receive mammography screenings every other year between the age 50 and 74 years. In addition, they argue that clinicians should not rely on clinical breast examination for cancer screening purposes.

The ACP reviewed various mammography guidelines from across the globe, including those from the American College of Radiology and the Canadian Task Force on Preventive Health Care, to come up with a consistent set of guidelines concerning women without increased risk of disease.

For women who do not have a family history of breast cancer or a genetic mutation known to increase their risk of developing breast cancer, the ACP now recommends clinicians discuss the pros and cons of breast cancer screening with women younger than 50 years of age, in order to help them better understand a patient’s preferences as well as to highlight why mammography may offer more potential harms than good to this particular age group. Women between 50-74 years should be offered screenings biennially-and women older than 75, with a life expectancy of 10 years or less, should forego mammography screenings altogether, according to the new guidelines.

The guidance statement notes that biennial mammography screening results in no significant difference in breast cancer mortality compared to annual screening-and reduces the risk of abnormal, but not cancerous results, that can result in unnecessary surgical procedures as well as potential psychological harms.

With this new guidance, based on converging research evidence, ACP hopes to reduce breast cancer overdiagnosis and improve clinical benefits to women at average risk for breast cancer across the country. In a related editorial, physicians discussed why these updated mammography guidelines are so important-and how they can better serve patients in the future.

Paving the Way for AI in Medical Imaging

A new research roadmap outlining how AI could be used in medical imaging to improve patient care has been published in the journal Radiology.  

Over the past few years, the use of deep learning algorithms to discriminate between images of benign and malignant masses in different forms of diagnostic imaging has increased-often showing accuracy as good as, if not better than, expert radiologists. Yet, much of this work is led by computer scientists, who may be unfamiliar of the specific research and clinical needs in medicine.

To directly address those needs, the National Institutes of Health (NIH), the Radiological Society of North American (RSNA), the American College of Radiology, and the Academy for Radiology and Biomedical Imaging Research met for a workshop in Bethesda, Maryland, in August 2018.

The published roadmap is a summary of the meeting group’s conclusions, suggesting ways that academic research laboratories, funding agencies, professional societies, and industry can better address specific knowledge gaps in this area. They suggest that key priorities for AI-related research in radiology should include:

• Image reconstruction methods that can easily and efficiently produce images that clinicians can interpret from source data;

• The creation of automated image labeling and annotation features, which should include information extracted from the imaging report;

• New machine learning methods that are tailored for clinical imaging data;

• Machine learning methods that can offer explanations and guidance regarding their identifications or discriminations to doctors and patients;

• And, finally, ways to easily de-identify imaging data in order to promote wider availability of clinical imaging data sets to researchers and clinicians.

In addressing such needs, the roadmap authors believe that future research can better inform AI methodologies that will support the creation of tools that can help radiologists better diagnose patients as well as track disease progression.

Tracking MS Progression with MRI

Researchers from Massachusetts General Hospital have now demonstrated that 7.0 Tesla (7T) magnetic resonance imaging (MRI) can better detect and track the cortical lesions present in multiple sclerosis (MS), according to a new study published in Radiology.

MS is a debilitating immune disorder, interrupting the critical communication between the brain and the body. Unfortunately, it can be difficult for physicians to track disease progression as the disease decimates myelin, the insulating fatty sheath that both protects and facilitates communication between nerve fibers. However, the improved sensitivity and spatial resolution of the high-strength 7T MRI scanner offered hope that researchers could better detect and follow the cortical lesions caused by this disease.

The researchers followed 33 patients with relapsing-remitting MS (20) and secondary progressive MS (13), as well as 10 age-matched healthy control participants, over a period of six years, acquiring images of both cortical and white matter lesions.

They discovered that the 7T MRI was able to detect more lesions than scanners with less sensitivity and resolution-and, in doing so, found that 25 of the MS patients had formed new lesions over the course of the study. In addition, the study authors highlighted a difference between the number of cortical and white matter lesions, finding that patients had, on average, about double the cortical lesions as white matter lesions.

And, perhaps most importantly, the researchers noted that the total volume of cortical lesions was highly correlated with symptoms and overall neurological disability in patients.

The researchers concluded that the lesions preferentially developed in cortical sulci, or the shallow grooves found in the cortex, suggesting that the disease-inducing inflammation may be occurring through a cerebrospinal fluid-mediated pathogenesis.

The findings also offer the potential that 7T-tracked cortical lesion accumulation can help physicians predict the disease’s progression and a patient’s expected disabilities over time.

The Association Between Obesity and Brain Structure

Dutch researchers have uncovered evidence that obesity is associated with reduced subcortical gray matter volume and a lower magnitude of white matter microstructure, potentially explaining the long-known link between being obesity and an increased risk for cognitive decline, according to a new study published in Radiology.

There are a number of health risks associated with being overweight-making obesity a global public health issue. But while doctors have seen a strong correlation between obesity and cognitive decline, they were unsure what the underlying neurobiological mechanisms might be.

Now, researchers from Leiden University Medical Center, the Center for Neurogenomics and Cognitive Research, and Erasmus University Medical Center, all in the Netherlands, have analyzed a large neuroimaging data set, using 3 Tesla (3T) multiparametric brain imaging to better understand what changes obesity might be making to the brain.

The imaging data set included more than 12,000 participants of both sexes with a mean age of 62 years. The researchers assessed associations between a person’s percentage of total body fat, measured by body impedance, and his or her brain structure, including overall and regional brain volumes as well as white matter microstructure. Major findings include:

• In men, the higher the total body fat, the smaller brain gray matter volume observed, with 5.5% greater total body fat being associated with a 3162 mm³ reduction in gray matter volume;

• Furthermore, a higher body fat percentage was linked with smaller volume in regions like the global pallidus, a region involved with voluntary movements, in both men and women;

• And, finally, total body fat percentage with positively associated with greater global fractional anistotropy in both men and women and lower global mean diffusivity in women, measures commonly used to differentiate older from younger brains.

Taken together, the researchers concluded that obesity does result in neurobiological changes to the brain’s structure, which could have implications for better understanding how a higher body fat percentage may lead to cognitive decline and dementia later in life.

Using 3D MR Fingerprinting to Detect Epileptic Lesions

Traditionally, physicians have had difficult detecting epileptic lesions using MRI techniques. Now, a new study in the Journal of Magnetic Resonance Imaging suggests that a unique magnetic resonance fingerprinting (MRF) technique can do better.

An international consortium of researchers from Case Western Reserve University, the Max Planck Institute for Biological Cybernetics, and the Cleveland Clinic, among others, have developed a high-resolution three-dimensional MRF fingerprinting protocol that offers clinicians T₁, T₂, proton density, and tissue fraction maps simultaneously from a single scan, allowing them to better identify and characterize lesions resulting from epilepsy.

The researchers scanned the brains of 15 patients with medically intractable epilepsy, as well as 5 healthy control volunteers. They compared their new protocol with traditional MRI methods to detect epileptic lesions.

In 4 patients, the group discovered previously undetected lesions with the MRF protocol-while showing consistent findings to typical MRI methods across the other 11 participants. This method, the authors suggest, offers clinicians the potential to observe hard-to-detect epileptogenic lesions in the future.