Combining FDG/PET with CT helps radiologists detect lymph node metastasis in high-risk endometrial cancer.
Adding fluorine 18 fluorodeoxyglucose (FDG) positron emission tomography (PET) to CT allows for better detection of lymph node (LN) metastasis in high-risk endometrial cancer, according to a study published in the journal Radiology. Researchers from Canada and the United States performed a prospective multicenter study to assess the diagnostic accuracy of FDG/PET combined with diagnostic contrast material–enhanced CT in detecting LN metastasis in high-risk endometrial cancer. The researchers gathered data from January 2010 and June 2013 of 215 patients who underwent PET/CT and pelvic and abdominal lymphadenectomy. A total of 207 enrolled patients (mean age 62.7 years) had PET/CT and pathologic examination results for the abdomen and pelvis. The researchers used data in all 23 patients with a positive abdominal examination and in 26 randomly selected patients with a negative abdominal examination for the central reader study. Seven independent blinded readers participated in different sessions one month apart to review diagnostic CT and PET/CT results. The accuracy was calculated at the participant level, correlating abdominal (right and left para-aortic and common iliac) and pelvic (right and left external iliac and obturator) LN regions with pathologic results, respecting laterality. Reader-average sensitivities, specificities, and areas under the receiver operating characteristic curve (AUCs) of PET/CT and diagnostic CT were compared. Power calculation was for sensitivity and specificity in the abdomen. The results showed the FDG PET/CT had satisfactory diagnostic accuracy in detecting abdominal LN metastasis: PET/CT Diagnostic CTSensitivity, detection of LNmetastasis abdomen 0.65 0.50Sensitivity, detection of LNin pelvis 0.65 0.48Specificity, abdomen 0.88 0.93Specificity, pelvis 0.93 0.89AUC, abdomen 0.78 0.74AUC, pelvis 0.82 0.73 The researchers concluded that compared with diagnostic CT alone, addition of PET to diagnostic CT significantly increased sensitivity in both the abdomen and pelvis while maintaining high 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.