Radiological Society of North America

Reducing low-value imaging has long been a priority for improving patient care and controlling costs. New research adds another dimension: unnecessary imaging can carry a measurable environmental toll, and it may be larger than clinicians might expect. “Healthcare-related emissions represent a substantial and growing share of our global carbon footprint,” said Isa Abdul Cader, MD, a diagnostic radiology resident at Loma Linda University in California.

Rising cancer incidence is driving interest in less invasive treatments. Histotripsy, a noninvasive, US-based ablation technique recently cleared by the U.S. Food and Drug Administration (FDA) for select liver tumors, is drawing growing attention as radiologists assess its role alongside current treatment modalities.

An AI-based algorithm that estimates lung nodule malignancy risk reached clinician-level accuracy with as little as 20% of the training data, according to a study published in Radiology: Artificial Intelligence. “Our findings suggest that robust, generalizable AI tools can be developed without massive datasets,” said lead author Bogdan Obreja, MSc, a PhD candidate at Radboud University Medical Center (RUMC) in Nijmegen, the Netherlands.

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.