FDG-PET provides physicians with survival predictions for patients with suspected atypical Parkinsonian syndrome.
FDG-PET can predict survival of patients with clinically suspected atypical Parkinsonian syndrome (APS), according to a study published in The Journal of Nuclear Medicine.
Researchers from Germany investigated the prognostic value of using FDG-PET compared with clinical diagnosis among patients with APS. Seventy-eight patients with suspected APS were enrolled in the study. All underwent initial FDG-PET imaging and were followed for 5.9 years. Of these patients, 44 were diagnosed with APS and 34 with Lewy body disease. Forty-four patients were still alive at 4.7 months (median) follow-up.
The results showed that patients who had been diagnosed with APS by PET or at one-year clinical follow-up had a median survival time of only 4.1 years, while the remaining patients, with Lewy body disease, had yet to reach median survival time.
The researchers concluded that use of FDG-PET provided an early predictor of survival in patients with clinically suspected APS. “This finding strongly supports the early inclusion of PET imaging in patient care,” they wrote.
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.