At last month's European Association of Nuclear Medicine Congress in Glasgow, Scotland, Dr. Henry Wagner presented preliminary results on the Molecular Coincidence Detection high-energy imaging technique under development by ADAC Laboratories of
At last month's European Association of Nuclear Medicine Congress in Glasgow, Scotland, Dr. Henry Wagner presented preliminary results on the Molecular Coincidence Detection high-energy imaging technique under development by ADAC Laboratories of Milpitas, CA. Wagner, who is a professor of radiation health sciences at John Hopkins Medical Institutions in Baltimore, said MCD could change the way patients with lung cancer are treated.
Wagner is participating in a multicenter clinical trial sponsored by ADAC to demonstrate the clinical effectiveness of MCD in oncology patients whose cancer may have metastasized. ADAC hopes that MCD can help differentiate between benign and malignant nodules, and determine whether surgical treatment is most appropriate.
In Wagner's trials, 35 patients were studied, with MCD producing a 96% sensitivity and an 80% specificity. Results from MCD could enable physicians to identify, prior to surgery, lung cancer patients who have a good chance of surgical cure and those who should be treated instead by radiation or chemotherapy, thus avoiding unnecessary surgery, according to Wagner.
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.