Journal of internal medicine
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Careful histopathologic examination remains the cornerstone in the diagnosis of the clinically and biologically heterogeneous group of lymphoid malignancies. However, recent advances in genomic and epigenomic characterization using high-throughput technologies have significantly improved our understanding of these tumors. ⋯ In this review, we will focus on clinically relevant diagnostic, prognostic, and predictive biomarkers identified in more common types of B-cell malignancies, and discuss how diagnostic assays designed for comprehensive molecular profiling may pave the way for the implementation of precision diagnostics/medicine approaches. We will also discuss future directions in this rapidly evolving field, including the application of single-cell sequencing and other omics technologies, to decipher clonal dynamics and evolution in lymphoid malignancies.
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Review
Application of precision medicine in clinical routine in haematology - challenges and opportunities.
Precision medicine is revolutionising patient care in cancer. As more knowledge is gained about the impact of specific genetic lesions on diagnosis, prognosis and treatment response, diagnostic precision and the possibility for optimal individual treatment choice have improved. Identification of hallmark genetic aberrations such as the BCR::ABL1 gene fusion in chronic myeloid leukaemia (CML) led to the rapid development of efficient targeted therapy and molecular follow-up, vastly improving survival for patients with CML during recent decades. ⋯ Further, experimental ways to guide the choice of targeted therapy for refractory patients are reviewed, such as functional precision medicine using drug profiling. An example of the use of pipeline studies where the treatment is chosen according to the molecular characteristics in rare solid malignancies is also provided. Finally, the future opportunities and remaining challenges of precision medicine in the real world are discussed.
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Non-alcoholic fatty liver disease (NAFLD) comprises a wide spectrum of pathologies ranging from non-alcoholic fatty liver (NAFL), characterized by simple steatosis without inflammation, to non-alcoholic steatohepatitis (NASH), characterized by steatosis of the liver accompanied by inflammation and hepatocyte ballooning, which can lead to advanced fibrosis, cirrhosis and hepatocellular carcinoma. Apart from lifestyle modifications such as weight loss, a Mediterranean diet and physical activity, only a few NAFLD-specific pharmacological treatment options such as Vitamin E and Pioglitazone are considered by current international guidelines. ⋯ Finally, knowledge about treating complications of end-stage liver disease due to NASH becomes an increasingly important cornerstone in the treatment of the broad disease spectrum of NAFLD. In this review, we summarize currently available and future treatment options for patients with NAFLD that may help internal medicine specialists treat the complete clinical spectrum of this highly prevalent liver disease.
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Nonalcoholic fatty liver disease (NAFLD) is defined by presence of steatosis in more than 5% of liver cells. The gold standard for diagnosis is liver biopsy, but this is seldom achieved due to costs and risk for side effects, and that is why the diagnosis is mostly made based on a combination of radiology and exclusion of other liver diseases. Disease severity staging can be noninvasively achieved with radiological exams such as elastography or blood-based markers that usually have lower sensitivity and specificity. ⋯ Many studies have not been able to adjust for key confounders, or suffer from different forms of bias. The clinical problem is nevertheless to identify persons with an increased risk for adverse hepatic and extrahepatic outcomes. We here discuss the evidence linking NAFLD to severe hepatic and extrahepatic outcomes.
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The deployment of machine learning for tasks relevant to complementing standard of care and advancing tools for precision health has gained much attention in the clinical community, thus meriting further investigations into its broader use. In an introduction to predictive modelling using machine learning, we conducted a review of the recent literature that explains standard taxonomies, terminology and central concepts to a broad clinical readership. Articles aimed at readers with little or no prior experience of commonly used methods or typical workflows were summarised and key references are highlighted. ⋯ Through two methodological deep dives using examples from precision psychiatry and outcome prediction after lymphoma, we highlight how the use of, for example, natural language processing can outperform established clinical risk scores and aid dynamic prediction and adaptive care strategies. Such realistic and detailed examples allow for critical analysis of the importance of new technological advances in artificial intelligence for clinical decision-making. New clinical decision support systems can assist in prevention and care by leveraging precision medicine.