Radiology in public focus

Press releases were sent to the medical news media for the following articles appearing in a recent issue of Radiology.

RSNA Publishes New QIBA Profile for Knee Cartilage MRI

New recommendations will help provide more reliable, reproducible results for MRI-based measurements of cartilage degeneration in the knee, helping to slow down disease and prevent progression to irreversible osteoarthritis, according to a special report published in Radiology.

For the study, Majid Chalian, MD, assistant professor of radiology and section head of musculoskeletal imaging and intervention in the Department of Radiology at the University of Washington in Seattle, and colleagues from the RSNA Musculoskeletal Biomarkers Committee of the Quantitative Imaging Biomarkers Alliance (QIBA) collaborated to create a QIBA profile for cartilage compositional imaging.

They analyzed major publications in the field and made several important determinations. MRI-based cartilage compositional analysis is a promising tool for revealing biochemical and microstructural changes in the early stages of osteoarthritis. T1rho and T2 mapping, have been established for assessing cartilage composition. T2 mapping is the only one currently available commercially. First, they found that cartilage T1rho and T2 values are measurable with 3T MRI with a variation of 4% to 5%. The committee also determined that a measured increase/decrease in T1rho and T2 value of 14% or more indicates a minimum detectable change, which can be used for defining response/ progression criteria for quantitative cartilage imaging.

“We are hoping that a machine learning approach can segment out the cartilage, and then we can apply this profile on the segmented cartilage so that we can make things go fast in busy clinical settings,” Dr. Chalian said. 

For More Information

Access the Radiology special report, "The QIBA Profile for MRI-based Compositional Imaging of Knee Cartilage."

RIPF Chalian Nov 21

Knee cartilage compartments with anatomic labels implemented in lateral (left side), central (middle), and medial (right side) MRI obtained with an intermediate weighted fat-saturated fast-spin-echo sequence (top row) and a spin-lattice relaxation time constant in rotating frame (T1r) magnetization-prepared angle-modulated partitioned k-space spoiled gradient echo snapshots sequence (bottom row, T1r maps). Study was performed without administration of intravenous gadolinium-based contrast material. The lateral femur (LF)/medial femur (MF) and lateral tibia (LT)/medial tibia (MT) can be further divided into subcompartments on the basis of meniscus anatomy according to Eckstein et al. P = patella, T = trochlea. 

Chalian et al, Radiology 2021 301; 7 ©RSNA 2021

Racial/Ethnic Disparities Persist in Lung Cancer Screening Eligibility

RIPF Narayan Nov 21

Bar graph shows proportion of survey respondents eligible for lung cancer screening, stratified according to race and ethnicity under previous and revised U.S. Preventive Services Task Force guidelines. All proportions are adjusted for complex Behavioral Risk Factor Surveillance System survey design elements.

Narayan et al, Radiology 2021 000:1–8 ©RSNA 2021

Revised guidelines for lung cancer screening eligibility are perpetuating disparities for racial/ethnic minorities, according to a new study in Radiology.

In March 2021, the United States Preventive Services Task Force (USPSTF) expanded eligibility for lung cancer screening with low-dose chest CT in high-risk individuals to reduce cancer-related mortality. The USPSTF lowered the threshold for lung cancer screening eligibility from age 55 to 50 and from at least 30 to at least 20 pack-years of smoking. The revised guidelines, made partly to address eligibility disparities in screening, raised concerns over their continued reliance on age and pack-year thresholds.

Anand K. Narayan, MD, PhD, radiologist and vice chair of equity in the Department of Radiology at the University of Wisconsin in Madison, and formerly of Massachusetts General Hospital (MGH) in Boston, Divya N. Chowdhry, MD, a breast imaging radiologist at MGH, and colleagues wanted to learn more about the impact of the changes on disparities in lung cancer screening eligibility.

Under the new guidelines, 14.7% of whites were eligible for lung cancer screening, compared with 9.1% of African Americans, 4.5% of Hispanics and 5.2% of Asian/Pacific Islanders. A better way to address the disparities, Dr. Narayan said, is through the incorporation of risk models into eligibility guidelines, such as variables like family history and the presence of chronic obstructive pulmonary disease. Social determinants of health like employment, education status and food insecurity might also play a role.

“If we put social determinants of health into our model, then we can more accurately reflect risk,” Dr. Narayan said. “It can give us tools to direct our resources toward patients in terms of how much risk they are experiencing and how much care they actually need. We can then target high-risk patients for more intensive screening and diagnostic services.”

For More Information

Access the Radiology study, "Racial and Ethnic Disparities in Lung Cancer Screening Eligibility."

Researchers Use Deep Learning to Predict Breast Cancer Risk

Compared with commonly used clinical risk factors, deep learning does a better job distinguishing between the mammograms of women who will later develop breast cancer and those who will not, according to a new study in Radiology.

For the study, John A. Shepherd, PhD, professor and researcher in the Population Sciences in the Pacific Program (Epidemiology) at the University of Hawaii Cancer Center in Honolulu, and colleagues trained the deep learning model to find signals in the mammogram that might be linked to increased cancer risk.

When they tested the deep learning-based model, it underperformed in assessing the risk factors for interval cancer risk, but it outperformed clinical risk factors including breast density in determining screening-detected cancer risk. The findings have significant implications for clinical practices in which breast density alone guides many management decisions. The deep learning model also has promise in supporting decisions about additional imaging with MRI and other modalities.

“By ranking mammograms in terms of the probability of seeing cancer in the image, AI is going to be a powerful second reading tool to help categorize mammograms,” Dr. Shepherd said.

For More Information

Access the Radiology study, “Deep Learning Predicts Interval and Screening-detected Cancer from Screening Mammograms: A Case-Case-Control Study in 6369 Women."

RIPF Shepherd Nov 21

Receiver operating characteristic curves show performance of the deep learning predictor as a yes-no decision between any group and the rest of the two groups. AUC = area under the receiver operating characteristic curve.

Zhu et al, Radiology 2021 301;7 ©RSNA 2021

Media Coverage of RSNA

In July and August, 1,090 RSNA-related news stories were tracked in the media. These stories had over 698 million audience impressions.

Coverage included the, Pittsburgh Post-Gazette, The Arizona Republic, Houston Chronicle, Applied Radiology, Healthcare Business News, Imaging Technology News, Diagnostic Imaging, Health Imaging News, Psychology Today, ScienceDaily, Pittsburgh Post-Gazette,, Houston Chronicle, Medscape, Radiology Business, and Health Management.

Lung Cancer Awareness Month: Share With Your Patients

Encourage your patients to visit, the public information website produced by RSNA and ACR, for easy-to-read patient information about the risk factors, available screening methods and treatment options for lung cancer.

November Public Information Outreach Activities Focus on Lung Cancer Awareness

In recognition of National Lung Cancer Awareness Month in November, RSNA is distributing public service announcements (PSAs) to inform patients about the risk factors, available screening methods and treatment options for the disease.