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Key Presentations Penn researchers will present results from clinical trials, including a national cooperative group study for esophageal cancer, a Phase I study using a new CAR T cell therapy for re-treatment in patients with lymphoma, and a multicenter study of a combination therapy for ovarian cancer. Also, Lynn M. CT in Room E451.
Because factors such as financial distress have been linked to higher mortality rates among patients with cancer -- in part because people may not get the screening exams they need and thus present with more advanced disease, wrote a team led by Samilia Obeng-Gyasi, MD, of the Ohio State University in Columbus.
CHICAGO -- A new breast cancer risk assessment technique that uses mammography biomarkers shows no racial bias, according to research presented November 29 at the RSNA meeting. The investigators used patient demographic data taken from electronicmedicalrecords and identified instances of cancer from a regional tumor registry.
By enabling personalized care, increasing disease awareness and streamlining processes, AI in healthcare creates a win-win situation for both patients and providers. Instead of just analyzing images, AI will increasingly combine them with a patient’s clinical history from their electronicmedicalrecords (EHR).
A group in Boston has validated an AI algorithm that could be useful for identifying non-smokers who are at high risk of lung cancer, according to a study to be presented at RSNA 2023 in Chicago. The primary outcome was six-year incident lung cancer, identified using International Classification of Disease codes.
milla1cf Wed, 11/22/2023 - 08:00 November 22, 2023 — Using a routine chest X-ray image, an artificial intelligence (AI) tool can identify non-smokers who are at high risk for lung cancer , according to a study being presented next week at the annual meeting of the Radiological Society of North America ( RSNA ). Walia, B.A. ,
PMID: 36111140 Clinical Question: In adult patients presenting to the emergency department with suspected biliary disease diagnosed by POCUS, does subsequent confirmatory RUS imaging change surgical management plan compared to decisions made based solely on POCUS findings? Trauma Surg Acute Care Open. 2022;7(1):e000944.
VIENNA - A deep-learning algorithm used with chest CT can help clinicians quantify patients' subcutaneous fat tissue levels on lung cancer screening -- and thus better predict disease outcomes, according to a presentation delivered on 29 February at ECR 2024. AT density, HU, mean -90.5 All-cause death 7% ASCVD 1.8% Lung cancer death 1.6%
Adding deep learning to CT imaging to assess changes in subcutaneous adipose tissue (SAT) over time could help predict outcomes among individuals vulnerable to lung cancer, according to research presented at the recent RSNA meeting. The team tracked SAT volume and density as measures of the SAT quality in each patient.
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