Theranostics
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With the recent developments in deep learning technologies, artificial intelligence (AI) has gradually been transformed from cutting-edge technology into practical applications. AI plays an important role in disease diagnosis and treatment, health management, drug research and development, and precision medicine. Interdisciplinary collaborations will be crucial to develop new AI algorithms for medical applications. In this paper, we review the basic workflow for building an AI model, identify publicly available databases of ocular fundus images, and summarize over 60 papers contributing to the field of AI development.
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Medical imaging can assess the tumor and its environment in their entirety, which makes it suitable for monitoring the temporal and spatial characteristics of the tumor. Progress in computational methods, especially in artificial intelligence for medical image process and analysis, has converted these images into quantitative and minable data associated with clinical events in oncology management. This concept was first described as radiomics in 2012. ⋯ Here, we review the recent methodological developments in radiomics, including data acquisition, tumor segmentation, feature extraction, and modelling, as well as the rapidly developing deep learning technology. Moreover, we outline the main applications of radiomics in diagnosis, treatment planning and evaluations in the field of oncology with the aim of developing quantitative and personalized medicine. Finally, we discuss the challenges in the field of radiomics and the scope and clinical applicability of these methods.
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Accurate localization of recurrent prostate cancer (PCa) is critical, especially if curative therapy is intended. With the aim to optimize target-to-background uptake ratio in 68Ga-PSMA-11 PET, we investigated the image quality and quantitative measures of regularized reconstruction by block-sequential regularized expectation maximization (BSREM). Methods: The study encompassed retrospective reconstruction and analysis of 20 digital time-of-flight (TOF) PET/CT examinations acquired 60 min post injection of 2 MBq/kg of 68Ga-PSMA-11 in PCa patients with biochemical relapse after primary treatment. ⋯ Decreased acquisition duration resulted in β-values of 800 to 1000 and 1200 to 1400 for 1 and 0.5 min per bed position, respectively, producing improved image quality measures compared with OSEM at a full acquisition duration of 2 min per bed position. The observer study showed a slight overall preference for BSREM β 900 although the interobserver variability was high. Conclusion: BSREM image reconstruction with β-values in the range of 400-900 resulted in lower BV and similar or improved SNR and SBR in comparison with OSEM.
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Malignant transformation of gastric cells is accompanied by the deregulated expression of glycosyltransferases leading to the biosynthesis of tumor-associated glycans such as the sialyl-Lewis X antigen (SLex). SLex presence on cell surface glycoconjugates increases the invasive capacity of gastric cancer cells and is associated with tumor metastasis. ST3Gal IV enzyme is involved in the synthesis of SLex antigen and overexpressed in gastric carcinomas. ⋯ This expression was associated with clinicopathological features of the tumors, including infiltrative pattern of tumor growth, presence of venous invasion and patient's poor survival. CEA immunoprecipitation from gastric carcinoma tissues also confirmed the presence of SLex. Conclusion: CEA is the major glycoprotein carrying SLex in gastric carcinoma and the conjoint detection of CEA-SLex is associated with aggressive tumor features highlighting its PLA detection as a biomarker of gastric cancer patient prognosis for theranostic applications.
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Hepatocellular carcinoma (HCC), the most prevalent form of liver cancer, is growing in incidence but treatment options remain limited, particularly for late stage disease. As liver cirrhosis is the principal risk state for HCC development, markers to detect early HCC within this patient population are urgently needed. Perturbation of epigenetic marks, such as DNA methylation (5mC), is a hallmark of human cancers, including HCC. ⋯ A panel of five CpGs (cg04645914, cg06215569, cg23663760, cg13781744, and cg07610777) yielded area under the receiver operating characteristic (AUROC) curves of 0.9525, 0.9714, and 0.9528 in cfDNA discovery and tissue validation cohorts 1 and 2, respectively. Validation of a 5-marker panel created from combining hypermethylated and hypomethylated CpGs in an independent cfDNA set by bisulfite pyrosequencing yielded an AUROC of 0.956, compared to the discovery AUROC of 0.996. Conclusion: Our finding that 5mC markers derived from primary tissue did not perform well in cfDNA, compared to those identified directly from cfDNA, reveals potential advantages of starting with cfDNA to discover high performing markers for liquid biopsy development.