Imaging confirms or excludes the presence of gangrenous appendicitis with high sensitivity and specificity.
Dual-energy CT with virtual monoenergetic and iodine overlay imaging accurately confirms or excludes the presence of gangrenous appendicitis, according to a study published in the American Journal of Roentgenology.
Researchers from Canada performed a retrospective study to determine if the use of dual-energy spectral techniques in CT can improve accuracy in the diagnosis of acute gangrenous appendicitis.
Related article: CT Technology: An Overview of the Latest Menu
A total of 209 patients with a pathologic diagnosis of appendicitis were included in the study. Two board-certified abdominal radiologists reviewed 120-kV simulated images, 40-keV virtual monoenergetic images, and color-coded iodine overlay images.
The results showed 44 patients (21.0%) had histopathologic results positive for gangrenous appendicitis. Other findings were:
All cases of gangrenous appendicitis had true-positive results of virtual monoenergetic and iodine overlay imaging. There were no false-negative results of virtual monoenergetic or iodine overlay imaging.
The researchers concluded that in cases of suspected appendicitis, dual-energy CT that includes virtual monoenergetic and iodine overlay imaging is accurate for confirming and excluding the presence of gangrenous appendicitis with high sensitivity and specificity.
What is the Best Use of AI in CT Lung Cancer Screening?
April 18th 2025In comparison to radiologist assessment, the use of AI to pre-screen patients with low-dose CT lung cancer screening provided a 12 percent reduction in mean interpretation time with a slight increase in specificity and a slight decrease in the recall rate, according to new research.
The Reading Room: Racial and Ethnic Minorities, Cancer Screenings, and COVID-19
November 3rd 2020In this podcast episode, Dr. Shalom Kalnicki, from Montefiore and Albert Einstein College of Medicine, discusses the disparities minority patients face with cancer screenings and what can be done to increase access during the pandemic.
Can CT-Based AI Radiomics Enhance Prediction of Recurrence-Free Survival for Non-Metastatic ccRCC?
April 14th 2025In comparison to a model based on clinicopathological risk factors, a CT radiomics-based machine learning model offered greater than a 10 percent higher AUC for predicting five-year recurrence-free survival in patients with non-metastatic clear cell renal cell carcinoma (ccRCC).
Could Lymph Node Distribution Patterns on CT Improve Staging for Colon Cancer?
April 11th 2025For patients with microsatellite instability-high colon cancer, distribution-based clinical lymph node staging (dCN) with computed tomography (CT) offered nearly double the accuracy rate of clinical lymph node staging in a recent study.