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Abstract Clinically, the body mass index remains the most frequently used metric of overall obesity, although it is flawed by its inability to account for different adipose (i.e., visceral, subcutaneous, and inter/intramuscular) compartments, as well as muscle mass. Numerous prior studies have demonstrated linkages between specific adipose or muscle compartments to outcomes of multiple diseases. Although there are no universally accepted standards for body composition measurement, many studies use a single slice at the L3 vertebral level. In this study, we use computed tomography (CT) studies from patients in The Cancer Genome Atlas (TCGA) to compare current L3-based techniques with volumetric techniques, demonstrating potential limitations with level-based approaches for assessing outcomes. In addition, we identify gene expression signatures in normal kidney that correlate with fat and muscle body composition traits that can be used to predict sex-specific outcomes in renal cell carcinoma.

Abstract Determination of body composition (the relative distribution of fat, muscle, and bone) has been used effectively to assess the risk of progression and overall clinical outcomes in different malignancies. Sarcopenia (loss of muscle mass) is especially associated with poor clinical outcomes in cancer. However, estimation of muscle mass through CT scan has been a cumbersome, manually intensive process requiring accurate contouring through dedicated personnel hours. Recently, fully automated technologies that can determine body composition in minutes have been developed and shown to be highly accurate in determining muscle, bone, and fat mass. We employed a fully automated technology, and analyzed images from a publicly available cancer imaging archive dataset (TCIA) and a tertiary academic center. The results show that adrenocortical carcinomas (ACC) have relatively sarcopenia compared to benign adrenal lesions. In addition, functional ACCs have accelerated sarcopenia compared to non-functional ACCs. Further longitudinal research might shed further light on the relationship between body component distribution and ACC prognosis, which will help us incorporate more nutritional strategies in cancer therapy.

Abstract Automated computed tomography (CT) scan segmentation (labelling of pixels according to tissue type) is now possible. This technique is being adapted to achieve three‐dimensional (3D) segmentation of CT scans, opposed to single L3‐slice alone. This systematic review evaluates feasibility and accuracy of automated segmentation of 3D CT scans for volumetric body composition (BC) analysis, as well as current limitations and pitfalls clinicians and researchers should be aware of. OVID Medline, Embase and grey literature databases up to October 2021 were searched. Original studies investigating automated skeletal muscle, visceral and subcutaneous AT segmentation from CT were included. Seven of the 92 studies met inclusion criteria. Variation existed in expertise and numbers of humans performing ground‐truth segmentations used to train algorithms. There was heterogeneity in patient characteristics, pathology and CT phases that segmentation algorithms were developed upon. Reporting of anatomical CT coverage varied, with confusing terminology. Six studies covered volumetric regional slabs rather than the whole body. One study stated the use of whole‐body CT, but it was not clear whether this truly meant head‐to‐fingertip‐to‐toe. Two studies used conventional computer algorithms. The latter five used deep learning (DL), an artificial intelligence technique where algorithms are similarly organized to brain neuronal pathways. Six of seven reported excellent segmentation performance (Dice similarity coefficients > 0.9 per tissue). Internal testing on unseen scans was performed for only four of seven algorithms, whilst only three were tested externally. Trained DL algorithms achieved full CT segmentation in 12 to 75 s versus 25 min for non‐DL techniques. DL enables opportunistic, rapid and automated volumetric BC analysis of CT performed for clinical indications. However, most CT scans do not cover head‐to‐fingertip‐to‐toe; further research must validate using common CT regions to estimate true whole‐body BC, with direct comparison to single lumbar slice. Due to successes of DL, we expect progressive numbers of algorithms to materialize in addition to the seven discussed in this paper. Researchers and clinicians in the field of BC must therefore be aware of pitfalls. High Dice similarity coefficients do not inform the degree to which BC tissues may be under‐ or overestimated and nor does it inform on algorithm precision. Consensus is needed to define accuracy and precision standards for ground‐truth labelling. Creation of a large international, multicentre common CT dataset with BC ground‐truth labels from multiple experts could be a robust solution.

Liver transplantation offers curative treatment to patients with acute and chronic end-stage liver disease. The impact of nutritional status on postoperative outcomes after liver transplantation remains poorly understood. The present study investigated the predictive value of radiologically assessed skeletal muscle index (SMI) and myosteatosis (MI) on postoperative outcomes.

BackgroundFrontotemporal dementia (FTD) represents a collection of neurobehavioral and neurocognitive syndromes that are associated with a significant degree of clinical, pathological, and genetic heterogeneity. Such heterogeneity hinders the identification of effective biomarkers, preventing effective targeted recruitment of participants in clinical trials for developing potential interventions and treatments. In the present study, we aim to automatically differentiate patients with three clinical phenotypes of FTD, behavioral-variant FTD (bvFTD), semantic variant PPA (svPPA), and nonfluent variant PPA (nfvPPA), based on their structural MRI by training a deep neural network (DNN).MethodsData from 277 FTD patients (173 bvFTD, 63 nfvPPA, and 41 svPPA) recruited from two multi-site neuroimaging datasets: the Frontotemporal Lobar Degeneration Neuroimaging Initiative and the ARTFL-LEFFTDS Longitudinal Frontotemporal Lobar Degeneration databases. Raw T1-weighted MRI data were preprocessed and parcellated into patch-based ROIs, with cortical thickness and volume features extracted and harmonized to control the confounding effects of sex, age, total intracranial volume, cohort, and scanner difference. A multi-type parallel feature embedding framework was trained to classify three FTD subtypes with a weighted cross-entropy loss function used to account for unbalanced sample sizes. Feature visualization was achieved through post-hoc analysis using an integrated gradient approach.ResultsThe proposed differential diagnosis framework achieved a mean balanced accuracy of 0.80 for bvFTD, 0.82 for nfvPPA, 0.89 for svPPA, and an overall balanced accuracy of 0.84. Feature importance maps showed more localized differential patterns among different FTD subtypes compared to groupwise statistical mapping.ConclusionIn this study, we demonstrated the efficiency and effectiveness of using explainable deep-learning-based parallel feature embedding and visualization framework on MRI-derived multi-type structural patterns to differentiate three clinically defined subphenotypes of FTD: bvFTD, nfvPPA, and svPPA, which could help with the identification of at-risk populations for early and precise diagnosis for intervention planning.

Abstract Background Pancreatic ductal adenocarcinoma (PDAC) is a lethal malignancy. Thus, there is an urgent need for safe and effective novel therapies. PDAC’s excessive reliance on glucose metabolism for its metabolic needs provides a target for metabolic therapy. Preclinical PDAC models have demonstrated that targeting the sodium-glucose co-transporter-2 (SGLT2) with dapagliflozin may be a novel strategy. Whether dapagliflozin is safe and efficacious in humans with PDAC is unclear. Methods We performed a phase 1b observational study (ClinicalTrials.gov ID NCT04542291; registered 09/09/2020) to test the safety and tolerability of dapagliflozin (5 mg p.o./day × 2 weeks escalated to 10 mg p.o./day × 6 weeks) added to standard Gemcitabine and nab-Paclitaxel (GnP) chemotherapy in patients with locally advanced and/or metastatic PDAC. Markers of efficacy including Response Evaluation Criteria in Solid Tumors (RECIST 1.1) response, CT-based volumetric body composition measurements, and plasma chemistries for measuring metabolism and tumor burden were also analyzed. Results Of 23 patients who were screened, 15 enrolled. One expired (due to complications from underlying disease), 2 dropped out (did not tolerate GnP chemotherapy) during the first 4 weeks, and 12 completed. There were no unexpected or serious adverse events with dapagliflozin. One patient was told to discontinue dapagliflozin after 6 weeks due to elevated ketones, although there were no clinical signs of ketoacidosis. Dapagliflozin compliance was 99.4%. Plasma glucagon increased significantly. Although abdominal muscle and fat volumes decreased; increased muscle-to-fat ratio correlated with better therapeutic response. After 8 weeks of treatment in the study, partial response (PR) to therapy was seen in 2 patients, stable disease (SD) in 9 patients, and progressive disease (PD) in 1 patient. After dapagliflozin discontinuation (and chemotherapy continuation), an additional 7 patients developed the progressive disease in the subsequent scans measured by increased lesion size as well as the development of new lesions. Quantitative imaging assessment was supported by plasma CA19-9 tumor marker measurements. Conclusions Dapagliflozin is well-tolerated and was associated with high compliance in patients with advanced, inoperable PDAC. Overall favorable changes in tumor response and plasma biomarkers suggest it may have efficacy against PDAC, warranting further investigation.

Abstract Background Computed tomography (CT) scans are routinely obtained in oncology and provide measures of muscle and adipose tissue predictive of morbidity and mortality. Automated segmentation of CT has advanced past single slices to multi‐slice measurements, but the concordance of these approaches and their associations with mortality after cancer diagnosis have not been compared. Methods A total of 2871 patients with colorectal cancer diagnosed during 2012–2017 at Kaiser Permanente Northern California underwent abdominal CT scans as part of routine clinical care from which mid‐L3 cross‐sectional areas and multi‐slice T12–L5 volumes of skeletal muscle (SKM), subcutaneous adipose (SAT), visceral adipose (VAT) and intermuscular adipose (IMAT) tissues were assessed using Data Analysis Facilitation Suite, an automated multi‐slice segmentation platform. To facilitate comparison between single‐slice and multi‐slice measurements, sex‐specific z‐scores were calculated. Pearson correlation coefficients and Bland–Altman analysis were used to quantify agreement. Cox models were used to estimate hazard ratios (HRs) and 95% confidence intervals (CIs) for death adjusting for age, sex, race/ethnicity, height, and tumour site and stage. Results Single‐slice area and multi‐slice abdominal volumes were highly correlated for all tissues (SKM R = 0.92, P

Neuroimaging-based brain-age estimation via machine learning has emerged as an important new approach for studying brain aging. The difference between one's estimated brain age and chronological age, the brain age gap (BAG), has been proposed as an Alzheimer's Disease (AD) biomarker. However, most past studies on the BAG have been cross-sectional. Quantifying longitudinal changes in an individual's BAG temporal pattern would likely improve prediction of AD progression and clinical outcome based on neurophysiological changes. To fill this gap, our study conducted predictive modeling using a large neuroimaging dataset with up to 8 years of follow-up to examine the temporal patterns of the BAG's trajectory and how it varies by subject-level characteristics (sex, APOEɛ4 carriership) and disease status. Specifically, we explored the pattern and rate of change in BAG over time in individuals who remain stable with normal cognition or mild cognitive impairment (MCI), as well as individuals who progress to clinical AD. Combining multimodal imaging data in a support vector regression model to estimate brain age yielded improved performance over single modality. Multilevel modeling results showed the BAG followed a linear increasing trajectory with a significantly faster rate in individuals with MCI who progressed to AD compared to cognitively normal or MCI individuals who did not progress. The dynamic changes in the BAG during AD progression were further moderated by sex and APOEɛ4 carriership. Our findings demonstrate the BAG as a potential biomarker for understanding individual specific temporal patterns related to AD progression.

Abstract Background Body composition from computed tomography (CT) scans is associated with cancer outcomes including surgical complications, chemotoxicity, and survival. Most studies manually segment CT scans, but Automatic Body composition Analyser using Computed tomography image Segmentation (ABACS) software automatically segments muscle and adipose tissues to speed analysis. Here, we externally evaluate ABACS in an independent dataset. Methods Among patients with non‐metastatic colorectal (n = 3102) and breast (n = 2888) cancer diagnosed from 2005 to 2013 at Kaiser Permanente, expert raters annotated tissue areas at the third lumbar vertebra (L3). To compare ABACS segmentation results to manual analysis, we quantified the proportion of pixel‐level image overlap using Jaccard scores and agreement between methods using intra‐class correlation coefficients for continuous tissue areas. We examined performance overall and among subgroups defined by patient and imaging characteristics. To compare the strength of the mortality associations obtained from ABACS's segmentations to manual analysis, we computed Cox proportional hazards ratios (HRs) and 95% confidence intervals (95% CI) by tertile of tissue area. Results Mean ± SD age was 63 ± 11 years for colorectal cancer patients and 56 ± 12 for breast cancer patients. There was strong agreement between manual and automatic segmentations overall and within subgroups of age, sex, body mass index, and cancer stage: average Jaccard scores and intra‐class correlation coefficients exceeded 90% for all tissues. ABACS underestimated muscle and visceral and subcutaneous adipose tissue areas by 1–2% versus manual analysis: mean differences were small at −2.35, −1.97 and −2.38 cm2, respectively. ABACS's performance was lowest for the

Childhood traumatic brain injury (TBI) is one of the most common causes of acquired disability and has significant implications for executive functions (EF), such as impaired attention, planning, and initiation that are predictive of everyday functioning. Evidence has suggested attentional features of executive functioning require behavioral flexibility that is dependent on frontostriatial circuitry. The purpose of this study was to evaluate surface-based deformation of a specific frontostriatial circuit in pediatric TBI and its role in EF. Regions of interest included: the dorsolateral prefrontal cortex (DLPFC), caudate nucleus, globus pallidus, and the mediodorsal nucleus of the thalamus (MD). T1-weighted magnetic resonance images were obtained in a sample of children ages 8–13 with complicated mild, moderate, or severe TBI (n = 32) and a group of comparison children with orthopedic injury (OI; n = 30). Brain regions were characterized using high-dimensional surface-based brain mapping procedures. Aspects of EF were assessed using select subtests from the Test of Everyday Attention for Children (TEA-Ch). General linear models tested group and hemisphere differences in DLPFC cortical thickness and subcortical shape of deep-brain regions; Pearson correlations tested relationships with EF. Main effects for group were found in both cortical thickness of the DLPFC (F1,60 = 4.30, p = 0.042) and MD mean deformation (F1,60 = 6.50, p = 0.01) all with lower values in the TBI group. Statistical surface maps revealed significant inward deformation on ventral-medial aspects of the caudate in TBI relative to OI, but null results in the globus pallidus. No significant relationships between EF and any region of interest were observed. Overall, findings revealed abnormalities in multiple aspects of a frontostriatial circuit in pediatric TBI, which may reflect broader pathophysiological mechanisms. Increased consideration for the role of deep-brain structures in pediatric TBI can aid in the clinical characterization of anticipated long-term developmental effects of these individuals.

Vertebra segmentation and identification is the crucial step for automatic spine analysis. Manual or semi-automatic segmentation and identification is a cumbersome approach used conventionally. This paper proposes an automatic method for accurate pixel-level labeling of vertebrae on CT images. The algorithm consists of two main steps: in the first step, a pixel-link convolutional neural network is trained to generate a binary mask for the vertebral column; and in the second step, a multi-label dilated residual network identifies the labels for each vertebra. The proposed model is evaluated on the VerSe-dataset which contains 374 CT scans. This includes scans with a variety of field-of-views and healthy/disease cases acquired from multiple scanners. The model is trained and evaluated on 2D coronal and sagittal slices extracted from the CT volume. Average dice scores of 0.89 and 0.90 were achieved on two test sets released as public and hidden test sets for VerSe-dataset. The mean pixel accuracy of the predicted segmentation maps for vertebra regions are 0.72–0.86 and 0.68–0.85 for test set 1 and test 2, respectively.

Objective: Our aim is to investigate patterns of brain glucose metabolism using fluorodeoxyglucose positron emission tomography (FDG-PET) in presymptomatic carriers of the C9orf72 repeat expansion to better understand the early preclinical stages of frontotemporal dementia (FTD). Methods: Structural MRI and FDG-PET were performed on clinically asymptomatic members of families with FTD caused by the C9orf72 repeat expansion (15 presymptomatic mutation carriers, C9orf72+; 20 non-carriers, C9orf72-). Regional glucose metabolism in cerebral and cerebellar gray matter was compared between groups. Results: The mean age of the C9orf72+ and C9orf72- groups were 45.3 ± 10.6 and 56.0 ± 11.0 years respectively, and the mean age of FTD onset in their families was 56 ± 7 years. Compared to non-carrier controls, the C9orf72+ group exhibited regional hypometabolism, primarily involving the cingulate gyrus, frontal and temporal neocortices (left > right) and bilateral thalami. Conclusions: The C9orf72 repeat expansion is associated with changes in brain glucose metabolism that are demonstrable up to 10 years prior to symptom onset and before changes in gray matter volume become significant. These findings indicate that FDG-PET may be a particularly sensitive and useful method for investigating and monitoring the earliest stages of FTD in individuals with this underlying genetic basis.

Down Syndrome is a chromosomal disorder that affects the development of cerebellar cortical lobules. Impaired neurogenesis in the cerebellum varies among different types of neuronal cells and neuronal layers. In this study, we developed an imaging analysis framework that utilizes gadolinium-enhanced ex vivo mouse brain MRI. We extracted the middle Purkinje layer of the mouse cerebellar cortex, enabling the estimation of the volume, thickness, and surface area of the entire cerebellar cortex, the internal granular layer, and the molecular layer in the Tc1 mouse model of Down Syndrome. The morphometric analysis of our method revealed that a larger proportion of the cerebellar thinning in this model of Down Syndrome resided in the inner granule cell layer, while a larger proportion of the surface area shrinkage was in the molecular layer.

Methods: Alzheimer's disease and Frontotemporal dementia are the first and third most common forms of dementia. Due to their similar clinical symptoms, they are easily misdiagnosed as each other even with sophisticated clinical guidelines. For disease-specific intervention and treatment, it is essential to develop a computer-aided system to improve the accuracy of their differential diagnosis. Recent advances in deep learning have delivered some of the best performance for medical image recognition tasks. However, its application to the differential diagnosis of AD and FTD pathology has not been explored.Approach: In this study, we proposed a novel deep learning based framework to distinguish between brain images of normal aging individuals and subjects with AD and FTD. Specifically, we combined the multi-scale and multi-type MRI-base image features with Generative Adversarial Network data augmentation technique to improve the differential diagnosis accuracy.Results: Each of the multi-scale, multitype, and data augmentation methods improved the ability for differential diagnosis for both AD and FTD. A 10-fold cross validation experiment performed on a large sample of 1,954 images using the proposed framework achieved a high overall accuracy of 88.28%.Conclusions: The salient contributions of this study are three-fold: (1) our experiments demonstrate that the combination of multiple structural features extracted at different scales with our proposed deep neural network yields superior performance than individual features; (2) we show that the use of Generative Adversarial Network for data augmentation could further improve the discriminant ability of the network regarding challenging tasks such as differentiating dementia sub-types; (3) and finally, we show that ensemble classifier strategy could make the network more robust and stable.

Fluorodeoxyglucose positron emission tomography (FDG-PET) imaging based 3D topographic brain glucose metabolism patterns from normal controls (NC) and individuals with dementia of Alzheimer's type (DAT) are used to train a novel multi-scale ensemble classification model. This ensemble model outputs a FDG-PET DAT score (FPDS) between 0 and 1 denoting the probability of a subject to be clinically diagnosed with DAT based on their metabolism profile. A novel 7 group image stratification scheme is devised that groups images not only based on their associated clinical diagnosis but also on past and future trajectories of the clinical diagnoses, yielding a more continuous representation of the different stages of DAT spectrum that mimics a real-world clinical setting. The potential for using FPDS as a DAT biomarker was validated on a large number of FDG-PET images (N=2984) obtained from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database taken across the proposed stratification, and a good classification AUC (area under the curve) of 0.78 was achieved in distinguishing between images belonging to subjects on a DAT trajectory and those images taken from subjects not progressing to a DAT diagnosis. Further, the FPDS biomarker achieved state-of-the-art performance on the mild cognitive impairment (MCI) to DAT conversion prediction task with an AUC of 0.81, 0.80, 0.77 for the 2, 3, 5 years to conversion windows respectively. Keywords: FDG-PET, Glucose metabolism, Dementia of Alzheimer's type (DAT), Multi-scale ensemble classifier

Frontotemporal dementia (FTD) is a neurodegenerative disease with a strong genetic basis. Understanding the structural brain changes during pre-symptomatic stages may allow for earlier diagnosis of patients suffering from FTD; therefore, we investigated asymptomatic members of FTD families with mutations in C9orf72 and granulin (GRN) genes. Clinically asymptomatic subjects from families with C9orf72 mutation (15 mutation carriers, C9orf72+; and 23 non-carriers, C9orf72−) and GRN mutations (9 mutation carriers, GRN+; and 15 non-carriers, GRN−) underwent structural neuroimaging (MRI). Cortical thickness and subcortical gray matter volumes were calculated using FreeSurfer. Group differences were evaluated, correcting for age, sex and years to mean age of disease onset within the subject's family. Mean age of C9orf72+ and C9orf72− were 42.6 ± 11.3 and 49.7 ± 15.5 years, respectively; while GRN+ and GRN− groups were 50.1 ± 8.7 and 53.2 ± 11.2 years respectively. The C9orf72+ group exhibited cortical thinning in the temporal, parietal and frontal regions, as well as reduced volumes of bilateral thalamus and left caudate compared to the entire group of mutation non-carriers (NC: C9orf72− and GRN− combined). In contrast, the GRN+ group did not show any significant differences compared to NC. C9orf72 mutation carriers demonstrate a pattern of reduced gray matter on MRI prior to symptom onset compared to GRN mutation carriers. These findings suggest that the preclinical course of FTD differs depending on the genetic basis and that the choice of neuroimaging biomarkers for FTD may need to take into account the specific genes involved in causing the disease. Keywords: Frontotemporal dementia, Magnetic resonance imaging, C9orf72 mutation, Granulin mutation, Cortical thickness, Subcortical volumes

Brain volume measurements extracted from structural MRI data sets are a widely accepted neuroimaging biomarker to study mouse models of neurodegeneration. Whether to acquire and analyze data in vivo or ex vivo is a crucial decision during the phase of experimental designs, as well as data analysis. In this work, we extracted the brain structures for both longitudinal in vivo and single-time-point ex vivo MRI acquired from the same animals using accurate automatic multi-atlas structural parcellation, and compared the corresponding statistical and classification analysis. We found that most gray matter structures volumes decrease from in vivo to ex vivo, while most white matter structures volume increase. The level of structural volume change also varies between different genetic strains and treatment. In addition, we showed superior statistical and classification power of ex vivo data compared to the in vivo data, even after resampled to the same level of resolution. We further demonstrated that the classification power of the in vivo data can be improved by incorporating longitudinal information, which is not possible for ex vivo data. In conclusion, this paper demonstrates the tissue-specific changes, as well as the difference in statistical and classification power, between the volumetric analysis based on the in vivo and ex vivo structural MRI data. Our results emphasize the importance of longitudinal analysis for in vivo data analysis.

Optical coherence tomography provides high-resolution 3D imaging of the posterior segment of the eye. However, quantitative morphological analysis, particularly relevant in retinal degenerative diseases such as glaucoma, has been confined to simple sectorization and averaging with limited spatial sensitivity for detection of clinical markers. In this paper, we present point-wise analysis and visualization of the retinal nerve fiber layer and choroid from cross-sectional data using functional shapes (fshape) registration. The fshape framework matches two retinas, or generates a mean of multiple retinas, by jointly optimizing the surface geometry and functional signals mapped on the surface. We generated group-wise mean retinal nerve fiber layer and choroidal surfaces with the respective layer thickness mapping and showed the difference by age (normal, younger vs. older) and by disease (age-matched older, normal vs. glaucomatous) in the two layers, along with a more conventional sector-based analysis for comparison. The fshape results visualized the detailed spatial patterns of the differences between the age-matched normal and glaucomatous retinal nerve fiber layers, with the glaucomatous layers most significantly thinner in the inferior region close to Bruch's membrane opening. Between the young and older normal cases, choroid was shown to be significantly thinner in the older subjects across all regions, but particularly in the nasal and inferior regions. The results demonstrate a comprehensive and detailed analysis with visualization of morphometric patterns by multiple factors.