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Highly multiplexed imaging reveals prognostic immune and stromal spatial biomarkers in breast cancer.
JCI Insight
January 2025
Department of Biomedical Engineering, Oregon Health and Science University, Portland, United States of America.
Spatial profiling of tissues promises to elucidate tumor-microenvironment interactions and generate prognostic and predictive biomarkers. We analyzed single-cell, spatial data from three multiplex imaging technologies: cyclic immunofluorescence (CycIF) data we generated from 102 breast cancer patients with clinical follow-up, and publicly available imaging mass cytometry and multiplex ion-beam imaging datasets. Similar single-cell phenotyping results across imaging platforms enabled combined analysis of epithelial phenotypes to delineate prognostic subtypes among estrogen-receptor positive (ER+) patients.
View Article and Find Full Text PDFThe predictive power of F-FDG PET/CT two-lesions radiomics and conventional models in classical Hodgkin's Lymphoma: a comparative retrospectively-validated study.
Ann Hematol
January 2025
Department of Radiology, Radiotherapy and Hematology, Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Italy.
In a previous preliminary study, radiomic features from the largest and the hottest lesion in baseline F-FDG PET/CT (bPET/CT) of classical Hodgkin's Lymphoma (cHL) predicted early response-to-treatment and prognosis. Aim of this large retrospectively-validated study is to evaluate the predictive role of two-lesions radiomics in comparison with other clinical and conventional PET/CT models. cHL patients with bPET/CT between 2010 and 2020 were retrospectively included and randomized into training-validation sets.
View Article and Find Full Text PDFBackground: Early identification of massive transfusion (MT) requirement in geriatric patients with severe trauma is challenging. Existing systems for predicting MT need in trauma patients have not been systematically evaluated for their relevance to the geriatric population. This study aimed to evaluate the predictive accuracy of initial vital signs and the Glasgow coma scale (GCS) in geriatric trauma patients for predicting MT.
View Article and Find Full Text PDFA Machine Learning Model Using Cardiac CT and MRI Data Predicts Cardiovascular Events in Obstructive Coronary Artery Disease.
Radiology
January 2025
From the Department of Cardiology (T.P., K.H., T.G., A.L., E.G., A.U., J.G.D., P.H.), MIRACL.ai (Multimodality Imaging for Research and Analysis Core Laboratory: and Artificial Intelligence) (T.P., S.T., K.H., T.G., A.L., E.G., A.U., J.G.D., P.H.), Inserm MASCOT-UMRS 942 (T.P., K.H., T.A.S., T.G., A.L., E.G., A.U., J.G.D., P.H.), and Department of Radiology (T.P., V.B., L.H., T.G.), Université Paris Cité, University Hospital of Lariboisière, Assistance Publique-Hôpitaux de Paris, Paris, France; Cardiovascular Magnetic Resonance Laboratory (T.P., T.H., T.U., F.S., S.C., P.G., J.G.) and Cardiac Computed Tomography Laboratory (T.P., T.H., T.L., B.C., T.U., F.S., S.C., H.B., A.N., M.A., P.G., J.G.), Hôpital Privé Jacques Cartier, Institut Cardiovasculaire Paris Sud, Ramsay Santé, 6 Avenue du Noyer Lambert, 91300 Massy, France; Scientific Partnerships, Siemens Healthcare France, Saint-Denis, France (S.T.); Department of Cardiology, Hôpital Universitaire de Bruxelles-Hôpital Erasme, Brussels, Belgium (A.U.); and Department of Cardiovascular Imaging, American Hospital of Paris, Neuilly, France (O.V., M.S.).
Background Multimodality imaging is essential for personalized prognostic stratification in suspected coronary artery disease (CAD). Machine learning (ML) methods can help address this complexity by incorporating a broader spectrum of variables. Purpose To investigate the performance of an ML model that uses both stress cardiac MRI and coronary CT angiography (CCTA) data to predict major adverse cardiovascular events (MACE) in patients with newly diagnosed CAD.
View Article and Find Full Text PDFStanniocalcin 1 in patients with refractory colorectal cancer treated with regorafenib: A post-hoc biomarker analysis of the TEXCAN and CORRECT trials.
Cancer Res Commun
January 2025
university hospital of besançon, Besancon, France.
Biomarkers for anti-angiogenic drugs in chemo-refractory metastatic colorectal cancer (mCRC) are lacking. We investigated the relationship between stanniocalcin 1 (STC1) and outcomes in patients treated with regorafenib in the TEXCAN and CORRECT trials. Baseline plasma STC1 protein levels were measured by ELISA from patients with chemo-refractory mCRC enrolled in TEXCAN (regorafenib n=48) and CORRECT (placebo n=211; regorafenib n=435).
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