Browsing by Author "Kraljevic, Ivana (14321919600)"

Objective: Artificial intelligence-based decision support systems (DSS) need to provide decisions that are not inferior to those given by experts in the field. Recommended insulin dose adjustments on the same individual data set were compared among multinational physicians, and with recommendations made by automated Endo-Digital DSS (ED-DSS). Research Design and Methods: This was a noninterventional study surveying 20 physicians from multinational academic centers. The survey included 17 data cases of individuals with type 1 diabetes who are treated with multiple daily insulin injections. Participating physicians were asked to recommend insulin dose adjustments based on glucose and insulin data. Insulin dose adjustments recommendations were compared among physicians and with the automated ED-DSS. The primary endpoints were the percentage of comparison points for which there was agreement on the trend of insulin dose adjustments. Results: The proportion of agreement and disagreement in the direction of insulin dose adjustment among physicians was statistically noninferior to the proportion of agreement and disagreement observed between ED-DSS and physicians for basal rate, carbohydrate-to insulin ratio, and correction factor (P < 0.001 and P ≤ 0.004 for all three parameters for agreement and disagreement, respectively). The ED-DSS magnitude of insulin dose change was consistently lower than that proposed by the physicians. Conclusions: Recommendations for insulin dose adjustments made by automatization did not differ significantly from recommendations given by expert physicians regarding the direction of change. These results highlight the potential utilization of ED-DSS as a useful clinical tool to manage insulin titration and dose adjustments. © Copyright 2022, Mary Ann Liebert, Inc.

Objective: Artificial intelligence-based decision support systems (DSS) need to provide decisions that are not inferior to those given by experts in the field. Recommended insulin dose adjustments on the same individual data set were compared among multinational physicians, and with recommendations made by automated Endo-Digital DSS (ED-DSS). Research Design and Methods: This was a noninterventional study surveying 20 physicians from multinational academic centers. The survey included 17 data cases of individuals with type 1 diabetes who are treated with multiple daily insulin injections. Participating physicians were asked to recommend insulin dose adjustments based on glucose and insulin data. Insulin dose adjustments recommendations were compared among physicians and with the automated ED-DSS. The primary endpoints were the percentage of comparison points for which there was agreement on the trend of insulin dose adjustments. Results: The proportion of agreement and disagreement in the direction of insulin dose adjustment among physicians was statistically noninferior to the proportion of agreement and disagreement observed between ED-DSS and physicians for basal rate, carbohydrate-to insulin ratio, and correction factor (P < 0.001 and P ≤ 0.004 for all three parameters for agreement and disagreement, respectively). The ED-DSS magnitude of insulin dose change was consistently lower than that proposed by the physicians. Conclusions: Recommendations for insulin dose adjustments made by automatization did not differ significantly from recommendations given by expert physicians regarding the direction of change. These results highlight the potential utilization of ED-DSS as a useful clinical tool to manage insulin titration and dose adjustments. © Copyright 2022, Mary Ann Liebert, Inc.

In the original publication of this article, upper and lower part of Table 2 was incorrectly formatted. The incorrect and correct version of Table 2 are shown in this correction article. The original article has been corrected. Relevant significantly enriched gene sets associated with the DMPs that differed between aggressive and metastatic PitNETs in the first surgery specimens (upper table) and in the entire cohort (lower table) Hypermethylated and positive enriched in PC pval padj NES size KEGG cell adhesion molecules CAMS 6.26E-07 5.63E-05 1.65 122 KEGG axon guidance 6.51E-07 5.63E-05 1.65 122 KEGG pathways in cancer 1.43E-06 8.24E-05 1.44 313 KEGG neuroactive ligand receptor interaction 6.39E-06 0.00028 1.44 249 KEGG focal adhesion 7.16E-05 0.0015 1.44 191 KEGG adherens junction 8.97E-05 0.0017 1.64 67 KEGG calcium signaling pathway 0.00025 0.0039 1.40 166 KEGG leukocyte transendothelial migration 0.0019 0.019 1.43 108 KEGG ECM receptor interaction 0.0019 0.019 1.49 83 KEGG gap junction 0.0063 0.036 1.40 83 KEGG axon guidance 1.60E-06 0.00028 1.53 122 KEGG calcium signaling pathway 3.22E-06 0.00028 1.44 166 KEGG neuroactive ligand receptor interaction 0.00010 0.0045 1.31 249 KEGG regulation of actin cytoskeleton 0.00013 0.0045 1.34 197 KEGG MAPK signaling pathway 0.00030 0.0066 1.29 251 KEGG cell adhesion molecules cams 0.0018 0.035 1.33 122 KEGG ECM receptor interaction 0.0034 0.049 1.36 83 KEGG Wnt signaling pathway 0.0052 0.064 1.27 145 KEGG Hedgehog signaling pathway 0.0057 0.064 1.40 55 KEGG leukocyte transendothelial migration 0.011 0.10 1.28 108 Relevant significantly enriched gene sets associated with the DMPs that differed between aggressive and metastatic PitNETs in the first surgery specimens (upper part of the table) and in the entire cohort (lower part of the table) Hypermethylated and positive enriched in PC pval padj NES size KEGG Cell adhesion molecules CAMS 6.26E-07 5.63E-05 1.65 122 KEGG Axon guidance 6.51E-07 5.63E-05 1.65 122 KEGG Pathways in cancer 1.43E-06 8.24E-05 1.44 313 KEGG Neuroactive ligand receptor interaction 6.39E-06 0.00028 1.44 249 KEGG Focal adhesion 7.16E-05 0.0015 1.44 191 KEGG Adherens junction 8.97E-05 0.0017 1.64 67 KEGG Calcium signaling pathway 0.00025 0.0039 1.40 166 KEGG Leukocyte transendothelial migration 0.0019 0.019 1.43 108 KEGG ECM receptor interaction 0.0019 0.019 1.49 83 KEGG Gap junction 0.0063 0.036 1.40 83 Hypermethylated and positive enriched in PC pval padj NES size KEGG Axon guidance 1.60E-06 0.00028 1.53 122 KEGG Calcium signaling pathway 3.22E-06 0.00028 1.44 166 KEGG Neuroactive ligand receptor interaction 0.00010 0.0045 1.31 249 KEGG Regulation of actin cytoskeleton 0.00013 0.0045 1.34 197 KEGG MAPK signaling pathway 0.00030 0.0066 1.29 251 KEGG Cell adhesion molecules cams 0.0018 0.035 1.33 122 KEGG ECM receptor interaction 0.0034 0.049 1.36 83 KEGG Wnt signaling pathway 0.0052 0.064 1.27 145 KEGG Hedgehog signaling pathway 0.0057 0.064 1.40 55 KEGG Leukocyte transendothelial migration 0.011 0.10 1.28 108 © The Author(s) 2024.

In the original publication of this article, upper and lower part of Table 2 was incorrectly formatted. The incorrect and correct version of Table 2 are shown in this correction article. The original article has been corrected. Relevant significantly enriched gene sets associated with the DMPs that differed between aggressive and metastatic PitNETs in the first surgery specimens (upper table) and in the entire cohort (lower table) Hypermethylated and positive enriched in PC pval padj NES size KEGG cell adhesion molecules CAMS 6.26E-07 5.63E-05 1.65 122 KEGG axon guidance 6.51E-07 5.63E-05 1.65 122 KEGG pathways in cancer 1.43E-06 8.24E-05 1.44 313 KEGG neuroactive ligand receptor interaction 6.39E-06 0.00028 1.44 249 KEGG focal adhesion 7.16E-05 0.0015 1.44 191 KEGG adherens junction 8.97E-05 0.0017 1.64 67 KEGG calcium signaling pathway 0.00025 0.0039 1.40 166 KEGG leukocyte transendothelial migration 0.0019 0.019 1.43 108 KEGG ECM receptor interaction 0.0019 0.019 1.49 83 KEGG gap junction 0.0063 0.036 1.40 83 KEGG axon guidance 1.60E-06 0.00028 1.53 122 KEGG calcium signaling pathway 3.22E-06 0.00028 1.44 166 KEGG neuroactive ligand receptor interaction 0.00010 0.0045 1.31 249 KEGG regulation of actin cytoskeleton 0.00013 0.0045 1.34 197 KEGG MAPK signaling pathway 0.00030 0.0066 1.29 251 KEGG cell adhesion molecules cams 0.0018 0.035 1.33 122 KEGG ECM receptor interaction 0.0034 0.049 1.36 83 KEGG Wnt signaling pathway 0.0052 0.064 1.27 145 KEGG Hedgehog signaling pathway 0.0057 0.064 1.40 55 KEGG leukocyte transendothelial migration 0.011 0.10 1.28 108 Relevant significantly enriched gene sets associated with the DMPs that differed between aggressive and metastatic PitNETs in the first surgery specimens (upper part of the table) and in the entire cohort (lower part of the table) Hypermethylated and positive enriched in PC pval padj NES size KEGG Cell adhesion molecules CAMS 6.26E-07 5.63E-05 1.65 122 KEGG Axon guidance 6.51E-07 5.63E-05 1.65 122 KEGG Pathways in cancer 1.43E-06 8.24E-05 1.44 313 KEGG Neuroactive ligand receptor interaction 6.39E-06 0.00028 1.44 249 KEGG Focal adhesion 7.16E-05 0.0015 1.44 191 KEGG Adherens junction 8.97E-05 0.0017 1.64 67 KEGG Calcium signaling pathway 0.00025 0.0039 1.40 166 KEGG Leukocyte transendothelial migration 0.0019 0.019 1.43 108 KEGG ECM receptor interaction 0.0019 0.019 1.49 83 KEGG Gap junction 0.0063 0.036 1.40 83 Hypermethylated and positive enriched in PC pval padj NES size KEGG Axon guidance 1.60E-06 0.00028 1.53 122 KEGG Calcium signaling pathway 3.22E-06 0.00028 1.44 166 KEGG Neuroactive ligand receptor interaction 0.00010 0.0045 1.31 249 KEGG Regulation of actin cytoskeleton 0.00013 0.0045 1.34 197 KEGG MAPK signaling pathway 0.00030 0.0066 1.29 251 KEGG Cell adhesion molecules cams 0.0018 0.035 1.33 122 KEGG ECM receptor interaction 0.0034 0.049 1.36 83 KEGG Wnt signaling pathway 0.0052 0.064 1.27 145 KEGG Hedgehog signaling pathway 0.0057 0.064 1.40 55 KEGG Leukocyte transendothelial migration 0.011 0.10 1.28 108 © The Author(s) 2024.

Aggressive pituitary neuroendocrine tumors (PitNETs)/adenomas are characterized by progressive growth despite surgery and all standard medical therapies and radiotherapy. A subset will metastasize to the brain and/or distant locations and are termed metastatic PitNETs (pituitary carcinomas). Studies of potential prognostic markers have been limited due to the rarity of these tumors. A few recurrent somatic mutations have been identified, and epigenetic alterations and chromosomal rearrangements have not been explored in larger cohorts of aggressive and metastatic PitNETs. In this study, we performed genome-wide methylation analysis, including copy-number variation (CNV) calculations, on tumor tissue specimens from a large international cohort of 64 patients with aggressive (48) and metastatic (16) pituitary tumors. Twelve patients with non-invasive pituitary tumors (Knosp 0–2) exhibiting an indolent course over a 5 year follow-up served as controls. In an unsupervised hierarchical cluster analysis, aggressive/metastatic PitNETs clustered separately from benign pituitary tumors, and, when only specimens from the first surgery were analyzed, three separate clusters were identified: aggressive, metastatic, and benign PitNETs. Numerous CNV events affecting chromosomal arms and whole chromosomes were frequent in aggressive and metastatic, whereas benign tumors had normal chromosomal copy numbers with only few alterations. Genome-wide methylation analysis revealed different CNV profiles and a clear separation between aggressive/metastatic and benign pituitary tumors, potentially providing biomarkers for identification of these tumors with a worse prognosis at the time of first surgery. The data may refine follow-up routines and contribute to the timely introduction of adjuvant therapy in patients harboring, or at risk of developing, aggressive or metastatic pituitary tumors. © The Author(s) 2024.

Aggressive pituitary neuroendocrine tumors (PitNETs)/adenomas are characterized by progressive growth despite surgery and all standard medical therapies and radiotherapy. A subset will metastasize to the brain and/or distant locations and are termed metastatic PitNETs (pituitary carcinomas). Studies of potential prognostic markers have been limited due to the rarity of these tumors. A few recurrent somatic mutations have been identified, and epigenetic alterations and chromosomal rearrangements have not been explored in larger cohorts of aggressive and metastatic PitNETs. In this study, we performed genome-wide methylation analysis, including copy-number variation (CNV) calculations, on tumor tissue specimens from a large international cohort of 64 patients with aggressive (48) and metastatic (16) pituitary tumors. Twelve patients with non-invasive pituitary tumors (Knosp 0–2) exhibiting an indolent course over a 5 year follow-up served as controls. In an unsupervised hierarchical cluster analysis, aggressive/metastatic PitNETs clustered separately from benign pituitary tumors, and, when only specimens from the first surgery were analyzed, three separate clusters were identified: aggressive, metastatic, and benign PitNETs. Numerous CNV events affecting chromosomal arms and whole chromosomes were frequent in aggressive and metastatic, whereas benign tumors had normal chromosomal copy numbers with only few alterations. Genome-wide methylation analysis revealed different CNV profiles and a clear separation between aggressive/metastatic and benign pituitary tumors, potentially providing biomarkers for identification of these tumors with a worse prognosis at the time of first surgery. The data may refine follow-up routines and contribute to the timely introduction of adjuvant therapy in patients harboring, or at risk of developing, aggressive or metastatic pituitary tumors. © The Author(s) 2024.