April 23rd 2025
In a recent interview, Arjun Sahgal, M.D., discussed current and emerging research examining the potential of MRI-guided adaptive radiotherapy for treating glioblastomas.
Clinical Case Vignette Series™: 41st Annual Miami Breast Cancer Conference®
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Medical Crossfire®: How Can Thoracic Teams Facilitate Optimized Care of Patients With Stage I-III EGFR Mutation-Positive NSCLC?
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Lung Cancer Tumor Board®: How Do Emerging Data for ICIs, BiTEs, ADCs, and Targeted Strategies Address Unmet Needs in the Therapeutic Continuum for SCLC?
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26th Annual International Lung Cancer Congress®
July 25-26, 2025
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2025 International Symposium of Gastrointestinal Oncology (ISGIO)
September 12-13, 2025
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Lung Cancer Tumor Board: Enhancing Precision Medicine in NSCLC Through Advancements in Molecular Testing and Optimal Therapy Selection
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(CME Credit Only) Lung Cancer Tumor Board®: The Pivotal Role of Multimodal Therapy in Leveraging Immunotherapy for Stage I-III NSCLC When the Goal Is Cure
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(MOC and CME Credit) Lung Cancer Tumor Board®: The Pivotal Role of Multimodal Therapy in Leveraging Immunotherapy for Stage I-III NSCLC When the Goal Is Cure
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What is the Best Use of AI in CT Lung Cancer Screening?
In 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.
Meta-Analysis Shows Merits of AI with CTA Detection of Coronary Artery Stenosis and Calcified Plaque
Artificial intelligence demonstrated higher AUC, sensitivity, and specificity than radiologists for detecting coronary artery stenosis > 50 percent on computed tomography angiography (CTA), according to a new 17-study meta-analysis.
New bpMRI Study Suggests AI Offers Comparable Results to Radiologists for PCa Detection
Demonstrating no significant difference with radiologist detection of clinically significant prostate cancer (csPCa), a biparametric MRI-based AI model provided an 88.4 percent sensitivity rate in a recent study.
FDA Clears Emerging AI-Enabled Software for Cardiac Ultrasound
Recent research has demonstrated that the AI software HeartFocus enabled novice health-care providers to achieve greater than 85 percent agreement with expert sonographers in assessing echocardiographic parameters.
What New Research Reveals About Computed Tomography and Radiation-Induced Cancer Risk
In a recent interview, Rebecca Smith-Bindman, M.D., offered key insights on new research examining the link between computed tomography scans and projected future cases of radiation-induced cancer.
Can CT-Based AI Radiomics Enhance Prediction of Recurrence-Free Survival for Non-Metastatic ccRCC?
In 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).