Predicting whether a patient with cancer will benefit from immune checkpoint inhibitors (ICIs) without resorting to advanced genomic or immunologic assays is an important clinical need. To address this, we developed and evaluated SCORPIO, a machine learning system that utilizes routine blood tests (complete blood count and comprehensive metabolic profile) alongside clinical characteristics from 9,745 ICI-treated patients across 21 cancer types. SCORPIO was trained on data from 1,628 patients across 17 cancer types from Memorial Sloan Kettering Cancer Center.
View Article and Find Full Text PDFPurpose: Patients undergoing axillary lymph node dissection (ALND) for breast cancer face a high risk of lymphedema, further increased by high body mass index (BMI) and insulin resistance. GLP-1 receptor agonists (GLP-1RAs) have the potential to reduce these risk factors, but their role in lymphedema has never been investigated. The purpose of this study was to determine if GLP-RAs can reduce the risk of lymphedema in patients undergoing ALND.
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