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1دورية أكاديمية
المؤلفون: Rigamonti, Alessandra, Viatore, Marika, Polidori, Rebecca, Rahal, Daoud, Erreni, Marco, Fumagalli, Maria Rita, Zanini, Damiano, Doni, Andrea, Putignano, Anna Rita, Bossi, Paola, Voulaz, Emanuele, Alloisio, Marco, Rossi, Sabrina, Zucali, Paolo Andrea, Santoro, Armando, Balzano, Vittoria, Nisticò, Paola, Feuerhake, Friedrich, Mantovani, Alberto, Locati, Massimo, Marchesi, Federica
المصدر: Cancer research. - 84, 7 (2024) , 1165-1177, ISSN: 1538-7445
الوصف: Artificial intelligence (AI)–powered approaches are becoming increasingly used as histopathologic tools to extract subvisual features and improve diagnostic workflows. On the other hand, hi-plex approaches are widely adopted to analyze the immune ecosystem in tumor specimens. Here, we aimed at combining AI-aided histopathology and imaging mass cytometry (IMC) to analyze the ecosystem of non–small cell lung cancer (NSCLC). An AI-based approach was used on hematoxylin and eosin (H&E) sections from 158 NSCLC specimens to accurately identify tumor cells, both adenocarcinoma and squamous carcinoma cells, and to generate a classifier of tumor cell spatial clustering. Consecutive tissue sections were stained with metal-labeled antibodies and processed through the IMC workflow, allowing quantitative detection of 24 markers related to tumor cells, tissue architecture, CD45+ myeloid and lymphoid cells, and immune activation. IMC identified 11 macrophage clusters that mainly localized in the stroma, except for S100A8+ cells, which infiltrated tumor nests. T cells were preferentially localized in peritumor areas or in tumor nests, the latter being associated with better prognosis, and they were more abundant in highly clustered tumors. Integrated tumor and immune classifiers were validated as prognostic on whole slides. In conclusion, integration of AI-powered H&E and multiparametric IMC allows investigation of spatial patterns and reveals tissue relevant features with clinical relevance. Significance: Leveraging artificial intelligence–powered H&E analysis integrated with hi-plex imaging mass cytometry provides insights into the tumor ecosystem and can translate tumor features into classifiers to predict prognosis, genotype, and therapy response.
وصف الملف: pdf
الإتاحة: https://doi.org/10.1158/0008-5472.can-23-1698Test
https://freidok.uni-freiburg.de/data/248963Test
https://nbn-resolving.org/urn:nbn:de:bsz:25-freidok-2489633Test
https://freidok.uni-freiburg.de/dnb/download/248963Test -
2دورية أكاديمية
المؤلفون: Fumagalli, Ivan, Polidori, Rebecca, Renzi, Francesca, Fusini, Laura, Quarteroni, Alfio, Pontone, Gianluca, Vergara, Christian
المساهمون: I. Fumagalli, R. Polidori, F. Renzi, L. Fusini, A. Quarteroni, G. Pontone, C. Vergara
مصطلحات موضوعية: Transcatheter aortic valve implantation, computed tomography, fluid-structure interaction, image-based simulation, in silico predictive investigation, patient-specific analysi, Settore MED/11 - Malattie dell'Apparato Cardiovascolare
الوصف: Transcatheter aortic valve implantation (TAVI) is a minimally invasive intervention for the treatment of severe aortic valve stenosis. The main cause of failure is the structural deterioration of the implanted prosthetic leaflets, possibly inducing a valvular re-stenosis 5-10 years after the implantation. Based solely on pre-implantation data, the aim of this work is to identify fluid-dynamics and structural indices that may predict the possible valvular deterioration, in order to assist the clinicians in the decision-making phase and in the intervention design. Patient-specific, pre-implantation geometries of the aortic root, the ascending aorta, and the native valvular calcifications were reconstructed from computed tomography images. The stent of the prosthesis was modeled as a hollow cylinder and virtually implanted in the reconstructed domain. The fluid-structure interaction between the blood flow, the stent, and the residual native tissue surrounding the prosthesis was simulated by a computational solver with suitable boundary conditions. Hemodynamical and structural indicators were analyzed for five different patients that underwent TAVI - three with prosthetic valve degeneration and two without degeneration - and the comparison of the results showed a correlation between the leaflets' structural degeneration and the wall shear stress distribution on the proximal aortic wall. This investigation represents a first step towards computational predictive analysis of TAVI degeneration, based on pre-implantation data and without requiring additional peri-operative or follow-up information. Indeed, being able to identify patients more likely to experience degeneration after TAVI may help to schedule a patient-specific timing of follow-up.
العلاقة: info:eu-repo/semantics/altIdentifier/pmid/36971047; info:eu-repo/semantics/altIdentifier/wos/WOS:000964559200001; firstpage:1; lastpage:19; numberofpages:19; journal:INTERNATIONAL JOURNAL FOR NUMERICAL METHODS IN BIOMEDICAL ENGINEERING; https://hdl.handle.net/2434/962578Test; info:eu-repo/semantics/altIdentifier/scopus/2-s2.0-85152061458
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المؤلفون: Fumagalli, Ivan, Polidori, Rebecca, Renzi, Francesca, Fusini, Laura, Quarteroni, Alfio, Pontone, Gianluca, Vergara, Christian
مصطلحات موضوعية: cardiovascular-system, in-silico predictive investigation, wall shear-stress, image-based simulations, finite-element-method, fluid-structure interaction, computed tomography, algorithms, patient-specific analysis, transcatheter aortic valve implantation
الوصف: Transcatheter aortic valve implantation (TAVI) is a minimally invasive intervention for the treatment of severe aortic valve stenosis. The main cause of failure is the structural deterioration of the implanted prosthetic leaflets, possibly inducing a valvular re-stenosis 5-10 years after the implantation. Based solely on pre-implantation data, the aim of this work is to identify fluid-dynamics and structural indices that may predict the possible valvular deterioration, in order to assist the clinicians in the decision-making phase and in the intervention design. Patient-specific, pre-implantation geometries of the aortic root, the ascending aorta, and the native valvular calcifications were reconstructed from computed tomography images. The stent of the prosthesis was modeled as a hollow cylinder and virtually implanted in the reconstructed domain. The fluid-structure interaction between the blood flow, the stent, and the residual native tissue surrounding the prosthesis was simulated by a computational solver with suitable boundary conditions. Hemodynamical and structural indicators were analyzed for five different patients that underwent TAVI - three with prosthetic valve degeneration and two without degeneration - and the comparison of the results showed a correlation between the leaflets' structural degeneration and the wall shear stress distribution on the proximal aortic wall. This investigation represents a first step towards computational predictive analysis of TAVI degeneration, based on pre-implantation data and without requiring additional peri-operative or follow-up information. Indeed, being able to identify patients more likely to experience degeneration after TAVI may help to schedule a patient-specific timing of follow-up.
الوصول الحر: https://explore.openaire.eu/search/publication?articleId=od_______185::5155197c120c94908118749d42b7245fTest
https://infoscience.epfl.ch/record/302336Test
