Journal highlights
The following are highlights from the current issues of RSNA’s peer-reviewed journals.
Contribute to the Future of Pancreatic Imaging
Share your latest advances and discoveries in pancreatic imaging in the Radiology: Imaging Cancer new special collection on pancreatic adenocarcinoma, pancreatic neuroendocrine and hepatobiliary cancers.
We welcome original research articles, reviews and brief reports encompassing imaging and image-guided therapy from cell-based models with organoids, pre-clinical, translational and clinical research. Submissions may address innovations in imaging probes, imaging technologies, image-guided therapies, data analysis methods, public policy and AI/machine learning.
Accepted manuscripts are immediately published and will be presented on the Radiology: Imaging Cancer website.
Evaluating Neonatal Bowel Obstruction
Bowel obstruction is the leading surgical emergency in newborns, affecting approximately one in 2,000 births. These obstructions can be life-threatening and require accurate, timely diagnosis. Most causes of neonatal bowel obstruction differ from those seen in older children and adults. To optimize patient care, a clear understanding of the specific causes and the appropriate imaging examination is essential.
Neonates with bowel obstruction present with abdominal distention, feeding intolerance and vomiting, but clinical assessment alone may not localize the obstruction. Imaging, particularly abdominal radiography, plays a key role in distinguishing between upper and lower intestinal tract obstructions and guiding next steps.
In a new Radiology article, Nathan C. Hull, MD, of Mayo Clinic in Rochester, MN, and colleagues present a practical approach and techniques for evaluating congenital neonatal bowel obstruction. The authors review the spectrum of underlying causes, preferred modalities and characteristic features that distinguish these causes from other differential diagnoses.
“If an obstruction is suspected, evaluation and care of the neonate should occur in a center with pediatric specialists, including pediatric surgeons, pediatric radiologists, and neonatologists,” the authors write.
Read the full article, “How I Do It: Imaging Evaluation of Neonatal Bowel Instructions.” ”Follow the Radiology editor on X @RadiologyEditor.
Follow the Radiology editor on X @RadiologyEditor.
Radiographic signs of free air. (A) Supine frontal radiograph in a premature infant with free intraperitoneal air over the liver (arrow) and outlining of the falciform ligament (arrowheads), known as the football sign. (B) Frontal supine radiograph in a different premature infant with free intraperitoneal air around the central diaphragm (arrows), with visualization of both sides of the wall of bowel loops in the left upper abdomen (arrowheads), known as the Rigler sign. (C) Right-side-up lateral decubitus radiograph in a different neonate shows small triangular locules of free peritoneal air (arrowheads), known as the telltale triangle sign.
https://pubs.rsna.org/doi/10.1148/radiol.241778 © RSNA 2025
Advancing DCIS Care
Ductal carcinoma in situ (DCIS) is a noninvasive breast cancer confined to the ductal system and characterized by biologic, radiologic and clinical heterogeneity. Some lesions remain indolent and non–life-threatening, whereas others may progress to invasive ductal carcinoma. This variability makes DCIS challenging for radiologists to evaluate. Increased detection rates from screening programs have raised concerns about overdiagnosis and overtreatment, prompting a greater focus on understanding the biologic characteristics of DCIS.
A recent article in RadioGraphics highlights the complexity of diagnosing and managing DCIS. Authors led by Tatiana Cardoso de Mello Tucunduva, MD, of Grupo Fleury in São Paulo, describe the strengths and limitations of various imaging techniques including contrast-enhanced mammography, tomosynthesis, US and MRI. They also discuss the potential of radiomics, AI and other advanced tools for improving personalized care.
“With this knowledge, radiologists can effectively contribute to accurate identification and optimal therapeutic planning, such as ensuring that the imaging-based assessment of disease extent is adequate or identifying cases with potential for surgical upgrade,” the authors conclude.
Read the full article, “Advancements in DCIS Detection and Management.” This article is also available for CME on EdCentral.
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Comparison between mammography and MRI findings and extension. (A) Mammogram (magnified view) shows thin pleomorphic calcifications with segmental distribution (B) Maximum intensity projection reconstruction of a contrast-enhanced MRI image shows nonmass enhancement with segmental distribution and a clumped internal pattern. Thee enhancement includes a component corresponding to the calcifications, as well as a noncalcified component discernible solely on the MRI image.
https://pubs.rsna.org/doi/10.1148/rg.240174 © RSNA 2025
AI Forecasts Breast Cancer Chemo Response
Breast cancer, the most diagnosed cancer among women worldwide is a leading cause of cancer-related death. Neoadjuvant chemotherapy (NAC) is a key treatment, but its effectiveness varies. A method of noninvasively predicting pathologic complete response (pCR) to therapy could improve outcomes by guiding timely, personalized care.
In an article published in Radiology: Imaging Cancer, Maya Gilad, MSc, Israel Institute of Technology in Haifa, and colleagues conducted a retrospective study using multiparametric MRI data from a machine learning challenge dataset which included longitudinal breast MRI scans.
The researchers successfully developed a boosted decision tree model that used radiomic features extracted from physiologically decomposed diffusion-weighted imaging (PD-DWI) to predict pCR in the scans of 190 female patients who underwent MR imaging.
“A machine learning model using radiomics data derived from PD-DWI achieved higher performance than baseline and benchmark models in predicting pCR following neoadjuvant chemotherapy for breast cancer,” the researchers conclude.
Read the full article, “Radiomics-based Machine Learning Prediction of Neoadjuvant Chemotherapy Response in Breast Cancer Using Physiologically Decomposed Diffusion-weighted MRI.
Follow the Radiology: Imaging Cancer editor on X @RadIC_Editor.
RSNA Journals Impact Factors Rank High
All RSNA journals have increased their Journal Impact Factor from 2023 and are ranked in the top 25% in their category, according to the latest Clarivate Analytics Journal Citation Reports. Impact factor is used to measure the relevance and influence of academic journals based on citation data.
Radiology remains dominant, ranking first out of 212 journals in the Radiology, Nuclear Medicine and Medical Imaging category. It has an impact factor of 15.2, up from 12.1 in 2023 and was cited 60,450 times. Published regularly since 1923, Radiology is edited by Linda Moy, MD.
RadioGraphics, edited by Christine Cooky Menias, MD, achieved an impact factor of 5.5 with 15,431 citations.
This is the third year in a row that RSNA’s subspecialty journals, Radiology: Artificial Intelligence, Radiology: Cardiothoracic Imaging and Radiology: Imaging Cancer, have received impact factors.
Radiology: Artificial Intelligence, edited by Charles E. Kahn, Jr., MD, MS, had the largest growth, jumping five points from the previous year to an impact factor of 13.2 with 3,232 citations. Radiology: Imaging Cancer, edited by Gary D. Luker, MD, rose to a 6.3 impact factor with 713 total citations. Radiology: Cardiothoracic Imaging, edited by Suhny Abbara, MD, earned a 4.2 impact factor with 1,380 citations.