Radiological Society of North America

Murine Model Looks to Identify RIPF To test the hypothesis, Dr. Lo and team used PET imaging and CT to detect changes in two preclinical models. The first was a focal ablative RIPF model, in which fibrosis developed on an accelerated timeline over eight weeks. The second was a whole lung RIPF model in which fibrosis developed over six months. The timing of onset of expression of αvβ6 varied depending on the model. In the focal ablative model, the researchers saw uptake as early as four weeks.

Whole-body MRI (WB-MRI) is rapidly expanding as a screening tool for healthy patients, even as radiology leaders warn that evidence of its clinical benefit remains limited and its downstream impact uncertain. "Patients are getting whole-body MRI by the droves, so we can either let other people perform the study in a suboptimal way, or we offer it and do the best we can," said Preethi Guniganti, MD, assistant professor of radiology at Weill Cornell Medical College in New York City.

Total tumor volume (TTV), measured from prostate-specific membrane antigen (PSMA) PET imaging, provides a more accurate measure of disease burden and improves prediction of overall survival compared with counting metastases in patients with advanced prostate cancer treated with lutetium 177PSMA-617 (Lu-PSMA) therapy.

AI Plus MRI Data Supports Radiologists Breast MRI is the most sensitive modality for detecting DCIS, and when combined with multiparametric techniques, such as high spatiotemporal resolution perfusion imaging and diffusion imaging, it provides complementary anatomical and functional assessment. However, current radiological assessment of DCIS is largely limited to basic lesion shape and size, which does not fully capture its complexity.

CT imaging can help address shortcomings that exist with current methods of calculating biological age and provide quantitative biomarkers of aging associated with survival, cardiovascular events, metabolic disease and fragility fractures.

Using AI, researchers found that image-based risk scores for breast cancer derived from screening mammograms evolve over time and differ between women who develop cancer and those who do not, opening the door to a new era of dynamic breast cancer risk assessment. The research was published in Radiology. Deep learning models can now generate breast cancer risk scores directly from screening mammograms, using the entire image rather than a limited, predetermined feature such as density.

Turning Routine CT Data Into Reference Charts Reference charts have long been used to compare individual patients to population norms. Pediatric growth charts and MRI-based brain charts offer those such benchmarks, yet no equivalent existed for other anatomic structures across adulthood.

Melissa Davis, MD, MBA, is vice chair of medical informatics and an associate professor in the Department of Radiology and Biomedical Imaging at Yale University in New Haven, CT. She earned her bachelor’s degree from Wellesley College in Massachusetts and received her medical degree from the Medical University of South Carolina in Charleston. She went on to complete her diagnostic radiology residency and neuroradiology fellowship at the University of North Carolina in Chapel Hill.

Suyash Mohan, MD, is the 2026 RSNA William R. Eyler Editorial Fellow, and Ashwin Singh Parihar, MD, has been named the 2026 RSNA William W. Olmsted Trainee Editorial Fellow. Hyun Soo Ko, MD, was selected as the recipient of the 2026 RSNA Herbert Y. Kressel International Editorial Fellow.

All RSNA journals have increased their Journal Impact Factors, some by a wide margin, according to the Clarivate Analytics Journal Citation Reports. Journal Impact Factor uses citation data to measure the relevance and influence of academic journals.