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| http://dx.doi.org/10.1007/s12262-020-02571-7 | DOI Listing |
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Background: The progression of bladder cancer (BC) from non-muscle-invasive bladder cancer (NMIBC) to muscle-invasive bladder cancer (MIBC) significantly increases disease severity. Although the tumor microenvironment (TME) plays a pivotal role in this process, the heterogeneity of tumor cells and TME components remains underexplored.
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Nat Cancer
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Institute for Artificial Intelligence in Medicine, University Hospital Essen (AöR), Essen, Germany.
Despite advances in precision oncology, clinical decision-making still relies on limited variables and expert knowledge. To address this limitation, we combined multimodal real-world data and explainable artificial intelligence (xAI) to introduce AI-derived (AID) markers for clinical decision support. We used xAI to decode the outcome of 15,726 patients across 38 solid cancer entities based on 350 markers, including clinical records, image-derived body compositions, and mutational tumor profiles.
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Department of Visceral, Thoracic and Vascular Surgery, Faculty of Medicine, University Hospital Carl Gustav Carus, TUD Dresden University of Technology, Fetscherstraße 74, 01307, Dresden, Germany.
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Concordance of surgical treatment selection with the AUA guidelines for localized renal masses.
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College of Human Medicine, Michigan State University, Grand Rapids, MI; Division of Urology, Corewell Health West, Grand Rapids, MI. Electronic address:
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