July 26th 2024
A model that combines MRI-based deep learning radiomics and clinical factors demonstrated an 84.8 percent ROC AUC and a 92.6 percent precision-recall AUC for predicting perineural invasion in prostate cancer cases.
25th Annual International Lung Cancer Congress®
July 25-27, 2024
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2023 ASCO Direct™ Highlights: Practice-Changing Data From the Leading Oncology Conference
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6th Annual Precision Medicine Symposium: An Illustrated Tumor Board
October 18-19, 2024
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Community Practice Connections™: 24th Annual International Lung Cancer Congress®
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19th Annual New York Lung Cancers Symposium®
November 16, 2024
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Medical Crossfire®: How Do the Experts Select and Sequence Therapies to Optimize Patient Outcomes and Quality of Life in Advanced Prostate Cancer?
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Medical Crossfire®: How Does Recent Evidence on PARP Inhibitors and Combinations Inform Treatment Planning for Prostate Cancer Now and In the Future?
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Lung Cancer Tumor Board®: Enhancing Multidisciplinary Communication to Optimize Immunotherapy in Stage I-III NSCLC
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Can Polyenergetic Reconstruction Help Resolve Streak Artifacts in Photon Counting CT?
New research looking at photon-counting computed tomography (PCCT) demonstrated significantly reduced variation and tracheal air density attenuation with polyenergetic reconstruction in contrast to monoenergetic reconstruction on chest CT.
Systematic Review: PET/MRI May be More Advantageous than PET/CT in Cancer Imaging
While PET/MRI and PET/CT had comparable sensitivity for patient-level regional nodal metastases and lesion-level recurrence, the authors of a systematic review noted that PET/MRI had significantly higher accuracy in breast cancer and colorectal cancer staging.
AMA Issues New CPT Code for Intraoperative Fluorescence Breast Imaging
Going into effect in 2015, the Category III CPT add-on code may facilitate broader access to the use of fluorescence imaging for assessing margins after lumpectomies for signs of residual breast cancer.
Study Assesses Amyloid PET/MRI Combination in Detection of Alzheimer’s Disease
Emerging research revealed that a machine learning combination of amyloid beta PET and structural MRI demonstrated an 89 percent AUC in differentiating between older healthy patients and those with Alzheimer’s disease.