| 1. |
- Patel, Y., et al.
(författare)
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Virtual Ontogeny of Cortical Growth Preceding Mental Illness
- 2022
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Ingår i: Biological Psychiatry. - : Elsevier BV. - 0006-3223 .- 1873-2402. ; 92:4, s. 299-313
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Tidskriftsartikel (refereegranskat)abstract
- Background: Morphology of the human cerebral cortex differs across psychiatric disorders, with neurobiology and developmental origins mostly undetermined. Deviations in the tangential growth of the cerebral cortex during pre/perinatal periods may be reflected in individual variations in cortical surface area later in life. Methods: Interregional profiles of group differences in surface area between cases and controls were generated using T1-weighted magnetic resonance imaging from 27,359 individuals including those with attention-deficit/hyperactivity disorder, autism spectrum disorder, bipolar disorder, major depressive disorder, schizophrenia, and high general psychopathology (through the Child Behavior Checklist). Similarity of interregional profiles of group differences in surface area and prenatal cell-specific gene expression was assessed. Results: Across the 11 cortical regions, group differences in cortical area for attention-deficit/hyperactivity disorder, schizophrenia, and Child Behavior Checklist were dominant in multimodal association cortices. The same interregional profiles were also associated with interregional profiles of (prenatal) gene expression specific to proliferative cells, namely radial glia and intermediate progenitor cells (greater expression, larger difference), as well as differentiated cells, namely excitatory neurons and endothelial and mural cells (greater expression, smaller difference). Finally, these cell types were implicated in known pre/perinatal risk factors for psychosis. Genes coexpressed with radial glia were enriched with genes implicated in congenital abnormalities, birth weight, hypoxia, and starvation. Genes coexpressed with endothelial and mural genes were enriched with genes associated with maternal hypertension and preterm birth. Conclusions: Our findings support a neurodevelopmental model of vulnerability to mental illness whereby prenatal risk factors acting through cell-specific processes lead to deviations from typical brain development during pregnancy.
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| 2. |
- Landén, Mikael, 1966, et al.
(författare)
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Brain aging in major depressive disorder: results from the ENIGMA major depressive disorder working group
- 2021
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Ingår i: Molecular Psychiatry. - : Springer Science and Business Media LLC. - 1359-4184 .- 1476-5578. ; 26, s. 5124-5139
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Tidskriftsartikel (refereegranskat)abstract
- Major depressive disorder (MDD) is associated with an increased risk of brain atrophy, aging-related diseases, and mortality. We examined potential advanced brain aging in adult MDD patients, and whether this process is associated with clinical characteristics in a large multicenter international dataset. We performed a mega-analysis by pooling brain measures derived from T1-weighted MRI scans from 19 samples worldwide. Healthy brain aging was estimated by predicting chronological age (18–75 years) from 7 subcortical volumes, 34 cortical thickness and 34 surface area, lateral ventricles and total intracranial volume measures separately in 952 male and 1236 female controls from the ENIGMA MDD working group. The learned model coefficients were applied to 927 male controls and 986 depressed males, and 1199 female controls and 1689 depressed females to obtain independent unbiased brain-based age predictions. The difference between predicted “brain age” and chronological age was calculated to indicate brain-predicted age difference (brain-PAD). On average, MDD patients showed a higher brain-PAD of +1.08 (SE 0.22) years (Cohen’s d = 0.14, 95% CI: 0.08–0.20) compared with controls. However, this difference did not seem to be driven by specific clinical characteristics (recurrent status, remission status, antidepressant medication use, age of onset, or symptom severity). This highly powered collaborative effort showed subtle patterns of age-related structural brain abnormalities in MDD. Substantial within-group variance and overlap between groups were observed. Longitudinal studies of MDD and somatic health outcomes are needed to further assess the clinical value of these brain-PAD estimates. © 2020, The Author(s).
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| 3. |
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| 4. |
- Wierenga, Lara M., et al.
(författare)
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Greater male than female variability in regional brain structure across the lifespan
- 2022
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Ingår i: Human Brain Mapping. - : John Wiley & Sons. - 1065-9471 .- 1097-0193. ; 43:1, s. 470-499
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Tidskriftsartikel (refereegranskat)abstract
- For many traits, males show greater variability than females, with possible implications for understanding sex differences in health and disease. Here, the ENIGMA (Enhancing Neuro Imaging Genetics through Meta-Analysis) Consortium presents the largest-ever mega-analysis of sex differences in variability of brain structure, based on international data spanning nine decades of life. Subcortical volumes, cortical surface area and cortical thickness were assessed in MRI data of 16,683 healthy individuals 1-90 years old (47% females). We observed significant patterns of greater male than female between-subject variance for all subcortical volumetric measures, all cortical surface area measures, and 60% of cortical thickness measures. This pattern was stable across the lifespan for 50% of the subcortical structures, 70% of the regional area measures, and nearly all regions for thickness. Our findings that these sex differences are present in childhood implicate early life genetic or gene-environment interaction mechanisms. The findings highlight the importance of individual differences within the sexes, that may underpin sex-specific vulnerability to disorders.
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| 5. |
- Dima, Danai, et al.
(författare)
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Subcortical volumes across the lifespan : Data from 18,605 healthy individuals aged 3-90 years.
- 2022
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Ingår i: Human Brain Mapping. - : Wiley. - 1065-9471 .- 1097-0193. ; 43:1, s. 452-469
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Tidskriftsartikel (refereegranskat)abstract
- Age has a major effect on brain volume. However, the normative studies available are constrained by small sample sizes, restricted age coverage and significant methodological variability. These limitations introduce inconsistencies and may obscure or distort the lifespan trajectories of brain morphometry. In response, we capitalized on the resources of the Enhancing Neuroimaging Genetics through Meta-Analysis (ENIGMA) Consortium to examine age-related trajectories inferred from cross-sectional measures of the ventricles, the basal ganglia (caudate, putamen, pallidum, and nucleus accumbens), the thalamus, hippocampus and amygdala using magnetic resonance imaging data obtained from 18,605 individuals aged 3-90 years. All subcortical structure volumes were at their maximum value early in life. The volume of the basal ganglia showed a monotonic negative association with age thereafter; there was no significant association between age and the volumes of the thalamus, amygdala and the hippocampus (with some degree of decline in thalamus) until the sixth decade of life after which they also showed a steep negative association with age. The lateral ventricles showed continuous enlargement throughout the lifespan. Age was positively associated with inter-individual variability in the hippocampus and amygdala and the lateral ventricles. These results were robust to potential confounders and could be used to examine the functional significance of deviations from typical age-related morphometric patterns.
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| 6. |
- Frangou, Sophia, et al.
(författare)
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Cortical thickness across the lifespan : Data from 17,075 healthy individuals aged 3-90 years
- 2022
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Ingår i: Human Brain Mapping. - : John Wiley & Sons. - 1065-9471 .- 1097-0193. ; 43:1, s. 431-451
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Tidskriftsartikel (refereegranskat)abstract
- Delineating the association of age and cortical thickness in healthy individuals is critical given the association of cortical thickness with cognition and behavior. Previous research has shown that robust estimates of the association between age and brain morphometry require large-scale studies. In response, we used cross-sectional data from 17,075 individuals aged 3-90 years from the Enhancing Neuroimaging Genetics through Meta-Analysis (ENIGMA) Consortium to infer age-related changes in cortical thickness. We used fractional polynomial (FP) regression to quantify the association between age and cortical thickness, and we computed normalized growth centiles using the parametric Lambda, Mu, and Sigma method. Interindividual variability was estimated using meta-analysis and one-way analysis of variance. For most regions, their highest cortical thickness value was observed in childhood. Age and cortical thickness showed a negative association; the slope was steeper up to the third decade of life and more gradual thereafter; notable exceptions to this general pattern were entorhinal, temporopolar, and anterior cingulate cortices. Interindividual variability was largest in temporal and frontal regions across the lifespan. Age and its FP combinations explained up to 59% variance in cortical thickness. These results may form the basis of further investigation on normative deviation in cortical thickness and its significance for behavioral and cognitive outcomes.
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| 7. |
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| 8. |
- Belov, Vladimir, et al.
(författare)
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Multi-site benchmark classification of major depressive disorder using machine learning on cortical and subcortical measures
- 2024
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Ingår i: Scientific Reports. - : NATURE PORTFOLIO. - 2045-2322. ; 14:1
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Tidskriftsartikel (refereegranskat)abstract
- Machine learning (ML) techniques have gained popularity in the neuroimaging field due to their potential for classifying neuropsychiatric disorders. However, the diagnostic predictive power of the existing algorithms has been limited by small sample sizes, lack of representativeness, data leakage, and/or overfitting. Here, we overcome these limitations with the largest multi-site sample size to date (N = 5365) to provide a generalizable ML classification benchmark of major depressive disorder (MDD) using shallow linear and non-linear models. Leveraging brain measures from standardized ENIGMA analysis pipelines in FreeSurfer, we were able to classify MDD versus healthy controls (HC) with a balanced accuracy of around 62%. But after harmonizing the data, e.g., using ComBat, the balanced accuracy dropped to approximately 52%. Accuracy results close to random chance levels were also observed in stratified groups according to age of onset, antidepressant use, number of episodes and sex. Future studies incorporating higher dimensional brain imaging/phenotype features, and/or using more advanced machine and deep learning methods may yield more encouraging prospects.
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| 9. |
- McWhinney, Sean R, et al.
(författare)
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Principal component analysis as an efficient method for capturing multivariate brain signatures of complex disorders-ENIGMA study in people with bipolar disorders and obesity.
- 2024
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Ingår i: Human brain mapping. - 1097-0193 .- 1097-0193. ; 45:8
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Tidskriftsartikel (refereegranskat)abstract
- Multivariate techniques better fit the anatomy of complex neuropsychiatric disorders which are characterized not by alterations in a single region, but rather by variations across distributed brain networks. Here, we used principal component analysis (PCA) to identify patterns of covariance across brain regions and relate them to clinical and demographic variables in a large generalizable dataset of individuals with bipolar disorders and controls. We then compared performance of PCA and clustering on identical sample to identify which methodology was better in capturing links between brain and clinical measures. Using data from the ENIGMA-BD working group, we investigated T1-weighted structural MRI data from 2436 participants with BD and healthy controls, and applied PCA to cortical thickness and surface area measures. We then studied the association of principal components with clinical and demographic variables using mixed regression models. We compared the PCA model with our prior clustering analyses of the same data and also tested it in a replication sample of 327 participants with BD or schizophrenia and healthy controls. The first principal component, which indexed a greater cortical thickness across all 68 cortical regions, was negatively associated with BD, BMI, antipsychotic medications, and age and was positively associated with Li treatment. PCA demonstrated superior goodness of fit to clustering when predicting diagnosis and BMI. Moreover, applying the PCA model to the replication sample yielded significant differences in cortical thickness between healthy controls and individuals with BD or schizophrenia. Cortical thickness in the same widespread regional network as determined by PCA was negatively associated with different clinical and demographic variables, including diagnosis, age, BMI, and treatment with antipsychotic medications or lithium. PCA outperformed clustering and provided an easy-to-use and interpret method to study multivariate associations between brain structure and system-level variables. PRACTITIONER POINTS: In this study of 2770 Individuals, we confirmed that cortical thickness in widespread regional networks as determined by principal component analysis (PCA) was negatively associated with relevant clinical and demographic variables, including diagnosis, age, BMI, and treatment with antipsychotic medications or lithium. Significant associations of many different system-level variables with the same brain network suggest a lack of one-to-one mapping of individual clinical and demographic factors to specific patterns of brain changes. PCA outperformed clustering analysis in the same data set when predicting group or BMI, providing a superior method for studying multivariate associations between brain structure and system-level variables.
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| 10. |
- Valent, P, et al.
(författare)
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Refined diagnostic criteria and classification of mast cell leukemia (MCL) and myelomastocytic leukemia (MML) : a consensus proposal
- 2014
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Ingår i: Annals of Oncology. - : Elsevier BV. - 0923-7534 .- 1569-8041. ; 25:9, s. 1691-1700
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Tidskriftsartikel (refereegranskat)abstract
- Mast cell leukemia (MCL), the leukemic manifestation of systemic mastocytosis (SM), is characterized by leukemic expansion of immature mast cells (MCs) in the bone marrow (BM) and other internal organs; and a poor prognosis. In a subset of patients, circulating MCs are detectable. A major differential diagnosis to MCL is myelomastocytic leukemia (MML). Although criteria for both MCL and MML have been published, several questions remain concerning terminologies and subvariants. To discuss open issues, the EU/US-consensus group and the European Competence Network on Mastocytosis (ECNM) launched a series of meetings and workshops in 2011-2013. Resulting discussions and outcomes are provided in this article. The group recommends that MML be recognized as a distinct condition defined by mastocytic differentiation in advanced myeloid neoplasms without evidence of SM. The group also proposes that MCL be divided into acute MCL and chronic MCL, based on the presence or absence of C-Findings. In addition, a primary (de novo) form of MCL should be separated from secondary MCL that typically develops in the presence of a known antecedent MC neoplasm, usually aggressive SM (ASM) or MC sarcoma. For MCL, an imminent prephase is also proposed. This prephase represents ASM with rapid progression and 5%-19% MCs in BM smears, which is generally accepted to be of prognostic significance. We recommend that this condition be termed ASM in transformation to MCL (ASM-t). The refined classification of MCL fits within and extends the current WHO classification; and should improve prognostication and patient selection in practice as well as in clinical trials.
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| 11. |
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| 12. |
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| 13. |
- Gallo, Selene, et al.
(författare)
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Functional connectivity signatures of major depressive disorder: machine learning analysis of two multicenter neuroimaging studies
- 2023
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Ingår i: Molecular Psychiatry. - : SPRINGERNATURE. - 1359-4184 .- 1476-5578. ; 28:7, s. 3013-3022
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Tidskriftsartikel (refereegranskat)abstract
- The promise of machine learning has fueled the hope for developing diagnostic tools for psychiatry. Initial studies showed high accuracy for the identification of major depressive disorder (MDD) with resting-state connectivity, but progress has been hampered by the absence of large datasets. Here we used regular machine learning and advanced deep learning algorithms to differentiate patients with MDD from healthy controls and identify neurophysiological signatures of depression in two of the largest resting-state datasets for MDD. We obtained resting-state functional magnetic resonance imaging data from the REST-meta-MDD (N = 2338) and PsyMRI (N = 1039) consortia. Classification of functional connectivity matrices was done using support vector machines (SVM) and graph convolutional neural networks (GCN), and performance was evaluated using 5-fold cross-validation. Features were visualized using GCN-Explainer, an ablation study and univariate t-testing. The results showed a mean classification accuracy of 61% for MDD versus controls. Mean accuracy for classifying (non-)medicated subgroups was 62%. Sex classification accuracy was substantially better across datasets (73-81%). Visualization of the results showed that classifications were driven by stronger thalamic connections in both datasets, while nearly all other connections were weaker with small univariate effect sizes. These results suggest that whole brain resting-state connectivity is a reliable though poor biomarker for MDD, presumably due to disease heterogeneity as further supported by the higher accuracy for sex classification using the same methods. Deep learning revealed thalamic hyperconnectivity as a prominent neurophysiological signature of depression in both multicenter studies, which may guide the development of biomarkers in future studies.
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| 14. |
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| 15. |
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| 16. |
- Petrov, Dmitry, et al.
(författare)
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Machine Learning for Large-Scale Quality Control of 3D Shape Models in Neuroimaging
- 2017
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Ingår i: Machine learning in medical imaging. MLMI (Workshop). - Cham : Springer International Publishing. ; 10541, s. 371-378
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Tidskriftsartikel (refereegranskat)abstract
- As very large studies of complex neuroimaging phenotypes become more common, human quality assessment of MRI-derived data remains one of the last major bottlenecks. Few attempts have so far been made to address this issue with machine learning. In this work, we optimize predictive models of quality for meshes representing deep brain structure shapes. We use standard vertex-wise and global shape features computed homologously across 19 cohorts and over 7500 human-rated subjects, training kernelized Support Vector Machine and Gradient Boosted Decision Trees classifiers to detect meshes of failing quality. Our models generalize across datasets and diseases, reducing human workload by 30-70%, or equivalently hundreds of human rater hours for datasets of comparable size, with recall rates approaching inter-rater reliability.
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| 17. |
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| 18. |
- Sacchet, Matthew D., et al.
(författare)
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Cognitive and neural consequences of memory suppression in major depressive disorder
- 2017
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Ingår i: Cognitive, Affective, & Behavioral Neuroscience. - : SPRINGER. - 1530-7026 .- 1531-135X. ; 17:1, s. 77-93
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Tidskriftsartikel (refereegranskat)abstract
- Negative biases in cognition have been documented consistently in major depressive disorder (MDD), including difficulties in the ability to control the processing of negative material. Although negative information-processing biases have been studied using both behavioral and neuroimaging paradigms, relatively little research has been conducted examining the difficulties of depressed persons with inhibiting the retrieval of negative information from long-term memory. In this study, we used the think/no-think paradigm and functional magnetic resonance imaging to assess the cognitive and neural consequences of memory suppression in individuals diagnosed with depression and in healthy controls. The participants showed typical behavioral forgetting effects, but contrary to our hypotheses, there were no differences between the depressed and nondepressed participants or between neutral and negative memories. Relative to controls, depressed individuals exhibited greater activity in right middle frontal gyrus during memory suppression, regardless of the valence of the suppressed stimuli, and differential activity in the amygdala and hippocampus during memory suppression involving negatively valenced stimuli. These findings indicate that depressed individuals are characterized by neural anomalies during the suppression of long-term memories, increasing our understanding of the brain bases of negative cognitive biases in MDD.
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| 19. |
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| 20. |
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| 21. |
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| 22. |
- Frost Bellgowan, Julie, et al.
(författare)
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A Neural Substrate for Behavioral Inhibition in the Risk for Major Depressive Disorder
- 2015
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Ingår i: Journal of the American Academy of Child and Adolescent Psychiatry. - : ELSEVIER SCIENCE BV. - 0890-8567 .- 1527-5418. ; 54:10, s. 841-848
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Tidskriftsartikel (refereegranskat)abstract
- Objective: Behavioral inhibition (BI) is an early developing trait associated with cautiousness and development of clinical depression and anxiety. Little is known about the neural basis of BI and its predictive importance concerning risk for internalizing disorders. We looked at functional connectivity of the default-mode network (DMN) and salience network (SN), given their respective roles in self-relational and threat processing, in the risk for internalizing disorders, with an emphasis on determining the functional significance of these networks for BI. Method: We used functional magnetic resonance imaging to scan, during the resting state, children and adolescents 8 to 17 years of age who were either at high familial risk (HR; n = 16) or low familial risk (LR; n = 18) for developing clinical depression and/or anxiety. Whole-brain DMN and SN functional connectivity were estimated for each participant and compared across groups. We also compared the LR and HR groups on levels of BI and anxiety, and incorporated these data into follow-up neurobehavioral correlation analyses. Results: The HR group, relative to the LR group, showed significantly decreased DMN connectivity with the ventral striatum and bilateral sensorimotor cortices. Within the HR group, trait BI increased as DMN connectivity with the ventral striatum and sensorimotor cortex decreased. The HR and LR groups did not differ with respect to SN connectivity. Conclusion: Our findings show, in the risk for internalizing disorders, a negative functional relation between brain regions supporting self-relational processes and reward prediction. These findings represent a potential neural substrate for behavioral inhibition in the risk for clinical depression and anxiety.
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| 23. |
- Hamilton, Paul J., et al.
(författare)
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Effects of salience-network-node neurofeedback training on affective biases in major depressive disorder
- 2016
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Ingår i: Psychiatry Research. - : Elsevier. - 0925-4927 .- 1872-7506. ; 249, s. 91-96
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Tidskriftsartikel (refereegranskat)abstract
- Neural models of major depressive disorder (MDD) posit that over-response of components of the brains salience network (SN) to negative stimuli plays a crucial role in the pathophysiology of MDD. In the present proof-of-concept study, we tested this formulation directly by examining the affective consequences of training depressed persons to down-regulate response of SN nodes to negative material. Ten participants in the real neurofeedback group saw, and attempted to learn to down-regulate, activity from an empirically identified node of the SN. Ten other participants engaged in an equivalent procedure with the exception that they saw SN-node neurofeedback indices from participants in the real neurofeedback group. Before and after scanning, all participants completed tasks assessing emotional responses to negative scenes and to negative and positive self-descriptive adjectives. Compared to participants in the sham-neurofeedback group, from pre- to post-training, participants in the realneurofeedback group showed a greater decrease in SN-node response to negative stimuli, a greater decrease in self-reported emotional response to negative scenes, and a greater decrease in self-reported emotional response to negative self-descriptive adjectives. Our findings provide support for a neural formulation in which the SN plays a primary role in contributing to negative cognitive biases in MDD. (C) 2016 Elsevier Ireland Ltd. All rights reserved.
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| 24. |
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| 25. |
- Javaheripour, Nooshin, et al.
(författare)
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Altered resting-state functional connectome in major depressive disorder : a mega-analysis from the PsyMRI consortium
- 2021
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Ingår i: Translational Psychiatry. - : Springer Nature. - 2158-3188. ; 11:1
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Tidskriftsartikel (refereegranskat)abstract
- Major depressive disorder (MDD) is associated with abnormal neural circuitry. It can be measured by assessing functional connectivity (FC) at resting-state functional MRI, that may help identifying neural markers of MDD and provide further efficient diagnosis and monitor treatment outcomes. The main aim of the present study is to investigate, in an unbiased way, functional alterations in patients with MDD using a large multi-center dataset from the PsyMRI consortium including 1546 participants from 19 centers (). After applying strict exclusion criteria, the final sample consisted of 606 MDD patients (age: 35.8 +/- 11.9 y.o.; females: 60.7%) and 476 healthy participants (age: 33.3 +/- 11.0 y.o.; females: 56.7%). We found significant relative hypoconnectivity within somatosensory motor (SMN), salience (SN) networks and between SMN, SN, dorsal attention (DAN), and visual (VN) networks in MDD patients. No significant differences were detected within the default mode (DMN) and frontoparietal networks (FPN). In addition, alterations in network organization were observed in terms of significantly lower network segregation of SMN in MDD patients. Although medicated patients showed significantly lower FC within DMN, FPN, and SN than unmedicated patients, there were no differences between medicated and unmedicated groups in terms of network organization in SMN. We conclude that the network organization of cortical networks, involved in processing of sensory information, might be a more stable neuroimaging marker for MDD than previously assumed alterations in higher-order neural networks like DMN and FPN.
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| 26. |
- Sperr, Wolfgang R., et al.
(författare)
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International prognostic scoring system for mastocytosis (IPSM) : a retrospective cohort study
- 2019
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Ingår i: The Lancet Haematology. - : ELSEVIER SCI LTD. - 2352-3026. ; 6:12, s. E638-E649
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Tidskriftsartikel (refereegranskat)abstract
- Background The WHO classification separates mastocytosis into distinct variants, but prognostication remains a clinical challenge. The aim of this study was to improve prognostication for patients with mastocytosis. Methods We analysed data of the registry of the European Competence Network on Mastocytosis including 1639 patients (age 17-90 years) diagnosed with mastocytosis according to WHO criteria between Jan 12, 1978, and March 16, 2017. Univariate and multivariate analyses with Cox regression were applied to identify prognostic variables predicting survival outcomes and to establish a prognostic score. We validated this International Prognostic Scoring System in Mastocytosis (IPSM) with data of 462 patients (age 17-79 years) from the Spanish network Red Espanola de Mastocitosis diagnosed between Jan 22, 1998, and Nov 2, 2017. Findings The prognostic value of the WHO classification was confirmed in our study (p<0.0001). For patients with non-advanced mastocytosis (n=1380), we identified age 60 years or older (HR 10.75, 95% CI 5.68-20.32) and a concentration of alkaline phosphatase 100 U/L or higher (2.91, 1.60-5.30) as additional independent prognostic variables for overall survival. The resulting scoring system divided patients with non-advanced mastocytosis into three groups: low (no risk factors), intermediate 1 (one risk factor), and intermediate 2 (two risk factors). Overall survival and progression-free survival differed significantly among these groups (p<0.0001). In patients with advanced mastocytosis (n=259), age 60 years or older (HR 2.14, 95% CI 1.42-3.22), a concentration of tryptase 125 ng/mL or higher (1.81, 1.20-2.75), a leukocyte count of 16 x 10(9) per L or higher (1.88, 1.27-2.79), haemoglobin of 11 g/dL or lower (1.71, 1.13-2.57), a platelet count of 100 x 10(9) per L or lower (1.63, 1.13-2.34), and skin involvement (0.46, 0.30-0.69) were prognostic variables. Based on these variables, a separate score for advanced mastocytosis with four risk categories was established, with significantly different outcomes for overall survival and progression-free survival (p<0.0001). The prognostic value of both scores was confirmed in 413 patients with non-advanced disease and 49 with advanced mastocytosis from the validation cohort. Interpretation The IPSM scores for patients with non-advanced and advanced mastocytosis can be used to predict survival outcomes and guide treatment decisions. However, the predictive value of the IPSM needs to be confirmed in forthcoming trials. Copyright (C) 2019 Elsevier Ltd. All rights reserved.
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| 27. |
- Valent, Peter, et al.
(författare)
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Advances in the Classification and Treatment of Mastocytosis : Current Status and Outlook toward the Future.
- 2017
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Ingår i: Cancer Research. - 0008-5472 .- 1538-7445. ; 77:6, s. 1261-1270
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Tidskriftsartikel (refereegranskat)abstract
- Mastocytosis is a term used to denote a heterogeneous group of conditions defined by the expansion and accumulation of clonal (neoplastic) tissue mast cells in various organs. The classification of the World Health Organization (WHO) divides the disease into cutaneous mastocytosis, systemic mastocytosis, and localized mast cell tumors. On the basis of histomorphologic criteria, clinical parameters, and organ involvement, systemic mastocytosis is further divided into indolent systemic mastocytosis and advanced systemic mastocytosis variants, including aggressive systemic mastocytosis and mast cell leukemia. The clinical impact and prognostic value of this classification has been confirmed in numerous studies, and its basic concept remains valid. However, refinements have recently been proposed by the consensus group, the WHO, and the European Competence Network on Mastocytosis. In addition, new treatment options are available for patients with advanced systemic mastocytosis, including allogeneic hematopoietic stem cell transplantation and multikinase inhibitors directed against KIT D816V and other key signaling molecules. Our current article provides an overview of recent advances in the field of mastocytosis, with emphasis on classification, prognostication, and emerging new treatment options in advanced systemic mastocytosis.
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