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Chronic myeloid leukemia (CML) is defined by the presence of Philadelphia chromosome resulting from a reciprocal translocation between chromosomes 9 and 22 [t9;22] that gives rise to a BCR::ABL1 fusion gene. CML occurs in 3 different phases (chronic, accelerated, and blast phase) and is usually diagnosed in the chronic phase in developed countries. Tyrosine kinase inhibitor (TKI) therapy is a highly effective treatment option for patients with chronic phase–CML. The primary goal of TKI therapy in patients with chronic phase–CML is to prevent disease progression to accelerated phase–CML or blast phase–CML. Discontinuation of TKI therapy with careful monitoring is feasible in selected patients. This manuscript discusses the recommendations outlined in the NCCN Guidelines for the diagnosis and management of patients with chronic phase–CML.

AbstractBackgroundDecreased image self‐similarity of repeated FDG‐PET scans has been suggested to indicate increased risk for disease progression. Here, we compared several self‐similarity measures based on conventionally used voxel‐ and ROI‐based approaches and those based on patterns of cortical metabolism.MethodFrom the MCSA/ADRC, we included 376 cognitively unimpaired (CU) individuals and those with a clinical diagnosis of MCI (n = 55) or AD (n = 20) who had two FDG‐PET images within 2‐3 years and clinical follow‐up available. We compared image self‐similarity for three approaches: voxel, ROI and “eigenbrain” (Fig.1). For the voxel approach, FDG‐PET images were normalized to template space, grey matter masked, and intensity normalized to the pons to create standard uptake value ratios (SUVR) for each voxel. From these images, mean SUVR were extracted for 122 ROIs (MCALT‐ADIR122) for the ROI approach. For the “eigenbrain” approach, the SUVR images were transformed to a latent space embedding composed of 253 components (“eigenbrains”) and reflecting metabolism patterns, that was generated in an independent sample of 7008 FDG‐PET images. For each approach, image self‐similarity between baseline and follow‐up values (voxel, ROI or eigenbrain) was determined using three metrics (Pearson correlation, cosine, Euclidean distance). We computed AUC to determine which approach and metric would best discriminate between groups. We used Cox regression analyses to assess whether the metrics could predict disease progression.ResultComparing each distance metric across approaches, AUC was highest in differentiating clinical diagnoses using self‐similarity measures based on eigenbrains, then voxels, then ROIs (e.g., Euclidean distance CU vs AD AUC eigenbrain: 0.86, voxel: 0.75, ROI: 0.71; Fig.1). Across all approaches, Euclidean distance always discriminated best between classes. Eigenbrain‐based Euclidean distance was associated with risk for progression to MCI in CU (HR [95%CI]: 1.25 [1.05, 1.49]), and eigenbrain‐based cosine and Euclidean distances were associated with risk for progression to AD in MCI (1.61 [1.1, 2.34] and 1.53 [1.01, 2.31], respectively), while we found no associations for voxel‐ or ROI‐based distance measures.ConclusionEigenbrain‐based measures outperformed other, more conventional approaches in discriminating clinical diagnoses, and were associated with risk for disease progression. Our results suggest that decreased self‐similarity in metabolism patterns informs on disease stage and prognosis.

AbstractBackgroundQuantified accelerated aging, or “age gap,” defined as the difference between a person’s estimated age using a biological measurement, such as a brain image, and their true age, has strong associations with multiple neurodegenerative etiologies. As an alternative to imaging‐based measurements, in this study we developed an age prediction model based on gait patterns and used this model to quantify accelerated aging as an indicator of cognitive impairment and predictor of future cognitive decline.MethodA database of 16,917 pressure sensitive walkway exams from 5,307 participants in the Mayo Clinic Study of Aging was used to develop an age estimation model that included two main components. First, a novel dimensionality reduction and feature selection algorithm was used to extract gait features. Second, a regularized linear model was trained using these features to produce a sparse, clinically interpretable estimator. This model was used to calculate a gait age gap metric, which was tested for association with measures of cognitive impairment and future cognitive decline.ResultThe distributions of gait age gap in age‐matched participants defined as having no cognitive impairment (CU, n = 573), mild cognitive impairment (MCI, n = 520), or a clinical diagnosis of dementia due to Alzheimer’s (AD, n = 53) are shown in Fig 1a. A significant shift in the mean is observed between all three groups. Fig 1b. shows the distributions of baseline gait age gap values amongst individuals categorized by cognitive decline: stable CU (n = 644), MCI converters (n = 89), stable MCI (n = 78), and significant impairment converters (SI, n = 35). The baseline gait age gap in stable CU individuals was found to be significantly lower than in the already impaired or converter groups.ConclusionIn this study, we developed a model for estimating accelerated aging from gait variables in the form of a gait age gap metric. This metric was shown to be strongly associated with AD‐driven cognitive impairment as well as predictive of future cognitive decline. These results highlight the potential clinical utility of quantitative gait analysis in behavioral neurologic assessment of cognitive impairment in AD.

AbstractBackgroundGenetic mutations causative of familial fronto‐temporal lobar degeneration (f‐FTLD) are highly predictive of a specific proteinopathy, but there exists substantial inter‐individual variability in their system‐level pathophysiology, which can be assessed with 18Fluorodeoxyglucose‐positron emission tomography (FDG‐PET). Determining data‐driven patterns of network degeneration suggestive of underlying specific FTLD‐related genetic mutations is paramount to guide complex clinical decision making supported by the quantification of FDG‐PET.MethodWe collected clinical and FDG‐PET data from 39 patients with f‐FTLD, including 11 carrying the C9orf72 hexanucleotide expansion (1 asymptomatic, 9 with a predominant behavioral phenotype, 1 with a predominant language phenotype), 12 carrying a GRN mutation (1 asymptomatic, 7 behavioral, 4 language), and 16 carrying a MAPT mutation (6 asymptomatic, 10 behavioral). We performed a spectral covariance decomposition analysis between FDG‐PET images to yield latent patterns reflective of a distribution of metabolism opposing poles of relative hyper‐ and hypo‐metabolism across the entire brain (“eigenbrains” or EBs). We then conducted linear discriminant analyses (LDAs) to perform EB‐based multiclass predictions of genetic mutation and clinical phenotype.ResultThe spectral decomposition analysis yielded five eigenbrains, which explained 58.79% of the covariance between FDG‐PET images (Fig. 1). Gradients indicative of relative hypometabolism in left fronto‐temporo‐parietal areas distinguished GRN mutation carriers from other genetic mutations (EB1, EB2, EB4) and were associated with clinical phenotypes predominantly involving language (EB1, EB2, EB3). Conversely, those indicative of relative hypometabolism in prefrontal areas (EB3) and temporopolar areas (EB2) with a right hemispheric predominance were mostly associated with clinical phenotypes predominantly involving behavior/personality, and EB2 distinguished MAPT mutation carriers from other genetic mutations. The LDAs yielded accuracies of 80% and 75% in predicting genetic status and predominant clinical phenotype, respectively (Fig. 2).ConclusionFive EBs explained a high proportion of covariance in patterns of network degeneration across FTLD‐related genetic mutations. These EBs contained information relevant to the system‐level physiology associated with FTLD‐related pathogenic mechanisms and that allowed the prediction of clinical phenotype with relatively high accuracy. This supports the utility of FDG‐PET to guide the development of tools supporting clinical decision making and ongoing efforts to develop network‐based biomarkers to track disease progression and risk of phenoconversion in f‐FTLD.

AbstractBackgroundThere is a longstanding ambiguity regarding the clinical diagnosis of dementia syndromes predominantly targeting executive functions versus behavior/personality. This is due to a lack of knowledge about the macro‐scale anatomy underlying these symptomatologies, a partial overlap in clinical features, and the fact that a single underlying pathology can give rise to both phenotypes and vice‐versa.MethodWe included data from patients with genetic, biomarker, and/or autopsy evidence for fronto‐temporal lobar degeneration (FTLD) diagnosed with behavioral variant fronto‐temporal dementia (bvFTD, n = 30) and patients with biomarker and/or autopsy evidence for Alzheimer’s disease (AD) pathology diagnosed with either an initial and predominant dysexecutive (dAD, n = 52), behavioral (bvAD, n = 7), or amnestic (aAD, n = 28) syndrome. We assessed group‐wise differences in clinical/cognitive features and 18Fluorodeoxyglucose‐positron emission tomography (FDG‐PET) patterns. This was followed by a spectral covariance decomposition between FDG‐PET images to yield latent patterns of relative metabolism (“eigenbrains”). These eigenbrains were subsequently linked to clinical/cognitive data and meta‐analytic topics reflecting a wide range of mental abilities. We used a linear discriminant analysis (LDA) to perform eigenbrain‐based diagnostic predictions.ResultdAD and bvFTD patients were the youngest at symptom onset, followed by bvAD, then aAD. dAD patients had worse cognitive performance on nearly all cognitive domains compared to other groups. Hypometabolism was observed across associative cortices in dAD, temporo‐parietal areas in aAD, and fronto‐temporal areas in bvFTD and bvAD (Fig. 1). The spectral covariance decomposition yielded nine eigenbrains which explained 61% of the variance in patterns of FDG‐PET, and only the first three are presented (Fig. 2). These eigenbrains revealed that relative hypometabolism in association cortices, notably lateral parietal areas, associated with dysexecutive symptomatology and a lower likelihood of behavior/personality problems, whereas relative hypometabolism restricted to frontal and temporopolar areas showed opposite associations. The LDA yielded an accuracy of 82.1% in predicting diagnostic category (Fig. 3).ConclusionThis study suggests distinct macro‐scale underpinnings underlying predominant dysexecutive versus behavioral symptomatology in degenerative dementia phenotypes. This has implication for the implementation of clinical and research criteria for these dementia syndromes and highlights the importance of data‐driven techniques to inform the classification of degenerative diseases.

AbstractBackgroundPredictive models of tau‐PET accumulation have largely relied on the functional connectome to estimate its spatial patterns in Alzheimer’s disease (AD). Patterns of global functional organization represent a promising avenue as they encapsulate many biological properties relevant to neurodevelopment, cognition and degeneration. We compared approaches based on local connectivity (from task‐free fMRI) and global organization (from FDG‐PET) to predict regional tau‐PET across AD phenotypes.MethodWe included 430 cognitively unimpaired (CU) individuals (214 A+T‐, 216 A+T+) and 62 amnestic mild cognitive impairment (aMCI), 64 amnestic AD, 21 dysexecutive AD (dAD), 41 logopenic progressive aphasia (LPA) and 49 posterior cortical atrophy (PCA) patients (all A+T+) from the Alzheimer’s Disease Research Center, Mayo Clinic Study of Aging, and Neurodegenerative Research Groups (referred to as “Mayo cohort”). The connectivity approach consisted of assessing the relationship between a region’s tau level and its functional connectivity to the region of highest tau (epicenter). The global organization approach consisted of 1) performing a principal component analysis on 3000 FDG‐PET images that did not overlap with the Mayo cohort, 2) projecting FDG‐PET images of the Mayo cohort onto the low‐dimensional space generated in Step 1 and extracting corresponding weighted values across the first 100 components, 3) using these weighted values as predictors of regional tau‐PET in a linear model and determining optimal parameters based on predicted R 2 values, and 4) using the parameters from the model built in Step 3 to predict tau‐PET in the ADNI cohort (n = 86) (Fig.2).ResultThe connectivity‐based approach yielded adjusted R 2 values ranging from 0.19‐0.45 across diagnostic groups (Fig.1). The global organization approach yielded averaged adjusted R 2 and predicted R 2 values of 0.71 and 0.63, respectively, in the Mayo cohort. Out‐of‐sample median R 2 in the ADNI cohort was 0.41 (range 0.10‐0.60) (Fig.3).ConclusionAn unbiased approach based on the global functional organization of the brain and using an imaging modality widely used in clinical settings outperformed a connectome‐based one to predict regional tau‐PET across the AD phenotypic spectrum. Patterns of tau accumulation may better relate to the large‐scale physiology of the brain underlying specific mental functions rather than local connectivity factors.

AbstractBackgroundPredictive models of tau‐PET accumulation have largely relied on the functional connectome to estimate its spatial patterns in Alzheimer’s disease (AD). Patterns of global functional organization represent a promising avenue as they encapsulate many biological properties relevant to neurodevelopment, cognition and degeneration. We compared approaches based on local connectivity (from task‐free fMRI) and global organization (from FDG‐PET) to predict regional tau‐PET across AD phenotypes.MethodWe included 430 cognitively unimpaired (CU) individuals (214 A+T‐, 216 A+T+) and 62 amnestic mild cognitive impairment (aMCI), 64 amnestic AD, 21 dysexecutive AD (dAD), 41 logopenic progressive aphasia (LPA) and 49 posterior cortical atrophy (PCA) patients (all A+T+) from the Alzheimer’s Disease Research Center, Mayo Clinic Study of Aging, and Neurodegenerative Research Groups (referred to as “Mayo cohort”). The connectivity approach consisted of assessing the relationship between a region’s tau level and its functional connectivity to the region of highest tau (epicenter). The global organization approach consisted of 1) performing a principal component analysis on 3000 FDG‐PET images that did not overlap with the Mayo cohort, 2) projecting FDG‐PET images of the Mayo cohort onto the low‐dimensional space generated in Step 1 and extracting corresponding weighted values across the first 100 components, 3) using these weighted values as predictors of regional tau‐PET in a linear model and determining optimal parameters based on predicted R2 values, and 4) using the parameters from the model built in Step 3 to predict tau‐PET in the ADNI cohort (n = 86) (Fig.2).ResultThe connectivity‐based approach yielded adjusted R2 values ranging from 0.19‐0.45 across diagnostic groups (Fig.1). The global organization approach yielded averaged adjusted R2 and predicted R2 values of 0.71 and 0.63, respectively, in the Mayo cohort. Out‐of‐sample median R2 in the ADNI cohort was 0.41 (range 0.10‐0.60) (Fig.3).ConclusionAn unbiased approach based on the global functional organization of the brain and using an imaging modality widely used in clinical settings outperformed a connectome‐based one to predict regional tau‐PET across the AD phenotypic spectrum. Patterns of tau accumulation may better relate to the large‐scale physiology of the brain underlying specific mental functions rather than local connectivity factors.