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Breast cancer responds heterogeneously to treatment. This study evaluated the efficacy of image registration-based lesion tracking for assessing lesion-wise response in metastatic breast cancer (MBC). [18F]FDG PET/CT images from 15 patients with MBC from the multi-center IMPACT-MBC study were analyzed. Manual segmentation identified 644 lesions in baseline scans and 804 lesions in 2-week post-treatment scans. A deformable image registration method, validated for whole-body PET/CT scans of patients with cancer, was applied. Lesion tracking was performed using spatial overlap resulting from within-subject image registration, yielding a precision of 0.93 ± 0.07, a sensitivity of 0.87 ± 0.17, and an F1 score of 0.89 ± 0.10. Performance was consistent across lesion sites and lesion count-based disease burden. Sensitivity decreased for lesions under 1 mL. Image registration direction did not affect results. Between-subject registration enabled quantitative visualization and analysis of metabolic response patterns in bone lesions across the cohort. Results demonstrate the potential for automated, accurate lesion-wise response assessment, which may improve treatment monitoring in MBC.

BACKGROUND: Obesity has been associated with onset and progression of chronic kidney disease (CKD) but causal relationship remains uncertain. This study investigated how obesity causally affects estimated glomerular filtration rate.

METHODS: Cross-sectional and magnetic resonance imaging (MRI) data analyses were performed within the Prospective Investigation of Obesity, Energy, and Metabolism (POEM) study (502 participants, all aged 50 years). Additionally Mendelian randomization was performed using published summary data. Outcomes were creatinine- and cystatin C-based eGFR. Body mass index (BMI) and waist circumference (WC) were used as exposure variables in the cross-sectional and Mendelian randomization analyses. In the imaging data analyses, eGFR was regressed non-parametrically on tissue volume for each 3D voxel and visualized as a correlation "Imiomics" map.

RESULTS: Negative correlations were shown between cystatin C-based eGFR and BMI [beta = -0.190 (95% CI: -0.280 to -0.100)] and WC [beta = -0.160 (95% CI: -0.250 to -0.060)] in an adjusted model. In contrast, a positive association was found for creatinine-based eGFR [BMI beta = 1.20 (95% CI: 0.030 to 0.210) and WC beta = 0.160 (95% CI: 0.070 to 0.260)]. Similar patterns were found using MRI analysis (Imiomics map). Mendelian randomization implied a negative causal effect of obesity-related measures on cystatin C-based eGFR [BMI beta = -0.031 (95% CI: -0.037 to -0.026) and WC beta = -0.038 (95% CI: -0.045 to -0.031)], but no statistically significant effect was found for creatinine-based eGFR.

CONCLUSION: This study suggests a causal negative effect of obesity on cystatin C-based, but not creatinine-based eGFR. These findings warrant further research regarding estimations of kidney function when assessing obesity and CKD.

Background: Prone positioning is part of the management of acute respiratory distress syndrome (ARDS) and has been demonstrated to successfully improve the ventilation-perfusion match and reduce mortality in patients with severe respiratory failure. However, the effect of pronation on other organs than the lungs has not been widely studied. This study aimed to compare abdominal edema, perfusion and inflammation in supine and prone positioning in a porcine ARDS model.

Methods: Seventeen piglets were randomized into two groups: a supine group (n = 9) and a prone group (n = 8). Both groups received endotoxemic infusion and were observed for 6 h. Three animals per group underwent positron emission tomography-magnetic resonance imaging (PET-MRI) for imaging acquisition. Hemodynamic and respiratory parameters were recorded throughout the protocol. Inflammation was assessed by measuring cytokine concentrations in blood, ascites and the abdominal organs' tissue. The edema in abdominal organs was assessed by wet-dry ratio and pathophysiological analysis of tissue samples and by MRI and PET measurements from volumes of interest (VOIs) delineated in abdominal organ in MRI and PET images. The abdominal organs' perfusion was also assessed by MRI and PET measurements.

Results: The prone group had a faster CO2 washout and needed a lower positive end-expiratory pressure to maintain the desired oxygenation. In the prone group duodenal edema was lower (measured with wet-dry ratio) and renal perfusion, by both MRI and PET measurements, was lower than half compared to the supine group (MRI, perfusion fraction, f: supine group 0.13; prone group 0.03; p-value 0.002. PET Flow: supine group 1.7; prone group 0.4 ml/cm3/min; p-value 0.002). In addition, the histopathological samples of the kidneys showed a higher incidence and extent of glomerular thrombosis in the prone group.

Conclusions: In a porcine ARDS model, prone positioning was associated with enhanced glomerular thrombosis and low renal perfusion.

A cardiopulmonary exercise test (CPET) is a test assessing an individual's physiological response during exercise. Results may be affected by body composition, which is best evaluated through imaging techniques like magnetic resonance imaging (MRI). The aim of this study was to assess relationships between body composition and indices obtained from CPET. A total of 234 participants (112 female), all aged 50 years, underwent CPETs and whole-body MRI scans (> 1 million voxels). Voxel-wise statistical analysis of tissue volume and fat content was carried out with a method called Imiomics and related to the CPET indices peak oxygen consumption (V̇O_2peak), V̇O_2peak scaled by body weight (V̇O_2kg) and by total lean mass (V̇O_2lean), ventilatory efficiency (V̇E/V̇CO₂-slope), work efficiency (ΔV̇O₂/ΔWR) and peak exercise respiratory exchange ratio (RERpeak). V̇O_2peak showed the highest positive correlation with volume of skeletal muscle. V̇O_2kg negatively correlated with tissue volume in subcutaneous fat, particularly gluteal fat. RERpeak negatively correlated with tissue volume in skeletal muscle, subcutaneous fat, visceral fat and liver. Some associations differed between sexes: in females ΔV̇O₂/ΔWR correlated positively with tissue volume of subcutaneous fat and V̇E/V̇CO₂-slope with tissue volume of visceral fat, and, in males, V̇O_2peak correlated positively to lung volume. In conclusion, voxel-based Imiomics provided detailed insights into how CPET indices were related to the tissue volume and fat content of different body structures.

PET/MR systems demanded great efforts for accurate attenuation correction (AC) but differences in technology, geometry and hardware attenuation may also affect quantitative results. Dedicated PET systems using transmission-based AC are regarded as the gold standard for quantitative brain PET. The study aim was to investigate the agreement between quantitative PET outcomes from a PET/MR scanner against a stand-alone PET system.Nine patients with Parkinsonism underwent two 80-min dynamic PET scans with the dopamine transporter ligand [11C]PE2I. Images were reconstructed with resolution-matched settings using 68Ge-transmission (standalone PET), and zero-echo-time MR (PET/MR) scans for AC. Non-displaceable binding potential (BPND) and relative delivery (R1) were evaluated using volumes of interest and voxel-wise analysis.Correlations between systems were high (r >= 0.85) for both quantitative outcome parameters in all brain regions. Striatal BPND was significantly lower on PET/MR than on stand-alone PET (-7%). R1 was significantly overestimated in posterior cortical regions (9%) and underestimated in striatal (-9%) and limbic areas (-6%). The voxel-wise evaluation revealed that the MR-safe headphones caused a negative bias in both parametric BPND and R1 images. Additionally, a significant positive bias of R1 was found in the auditory cortex, most likely due to the acoustic background noise during MR imaging. The relative bias of the quantitative [11C]PE2I PET data acquired from a SIGNA PET/MR system was in the same order as the expected test-retest reproducibility of [11C]PE2I BPND and R1, compared to a stand-alone ECAT PET scanner. MR headphones and background noise are potential sources of error in functional PET/MR studies.

Early cancer detection, guided by whole-body imaging, is important for the overall survival and well-being of the patients. While various computer-assisted systems have been developed to expedite and enhance cancer diagnostics and longitudinal monitoring, the detection and segmentation of tumors, especially from whole-body scans, remain challenging. To address this, we propose a novel end -to-end automated framework that first generates a tumor probability distribution map (TPDM), incorporating prior information about the tumor characteristics (e.g. size, shape, location). Subsequently, the TPDM is integrated with a state-of-the-art 3D segmentation network along with the original PET/CT or PET/MR images. This aims to produce more meaningful tumor segmentation masks compared to using the baseline 3D segmentation network alone. The proposed method was evaluated on three independent cohorts (autoPET, CAR-T, cHL) of images containing different cancer forms, obtained with different imaging modalities, and acquisition parameters and lesions annotated by different experts. The evaluation demonstrated the superiority of our proposed method over the baseline model by significant margins in terms of Dice coefficient, and lesion-wise sensitivity and precision. Many of the extremely small tumor lesions (i.e. the most difficult to segment) were missed by the baseline model but detected by the proposed model without additional false positives, resulting in clinically more relevant assessments. On average, an improvement of 0.0251 (autoPET), 0.144 (CAR-T), and 0.0528 (cHL) in overall Dice was observed. In conclusion, the proposed TPDM-based approach can be integrated with any state-of-the-art 3D UNET with potentially more accurate and robust segmentation results.

Obesity is associated with an increased risk of morbidity and mortality. Achieving a healthy body composition, which involves maintaining a balance between fat and muscle mass, is important for metabolic health and preventing chronic diseases. Computed tomography (CT) imaging offers detailed insights into the body’s internal structure, aiding in understanding body composition and its related factors. In this feasibility study, we utilized CT image data from 2,724 subjects from the large metabolic health cohort studies SCAPIS and IGT. We train and evaluate an uncertainty-aware deep regression based ResNet-50 network, which outputs its prediction as mean and variance, for quantification of cross-sectional areas of liver, visceral adipose tissue (VAT), and thigh muscle. This was done using collages of three single-slice CT images from the liver, abdomen, and thigh regions. The model demonstrated promising results with the evaluation metrics – including R-squared (R²) and mean absolute error (MAE) for predictions. Additionally, for interpretability, the model was evaluated with saliency analysis based on Grad-CAM (Gradient-weighted Class Activation Mapping) at stages 2, 3, and 4 of the network. Deformable image registration to a template subject further enabled cohort saliency analysis that provide group-wise visualization of image regions of importance for associations to biomarkers of interest. We found that the networks focus on relevant regions for each target, according to prior knowledge.

Objectives Experimental studies indicate a role for galectin-1 and galectin-3 in metabolic disease, but clinical evidence from larger populations is limited.

Methods We measured circulating levels of galectin-1 and galectin-3 in the Prospective investigation of Obesity, Energy and Metabolism (POEM) study, participants (n = 502, all aged 50 years) and characterized the individual association profiles with metabolic markers, including clinical measures, metabolomics, adipose tissue distribution (Imiomics) and proteomics.

Results Galectin-1 and galectin-3 were associated with fatty acids, lipoproteins and triglycerides including lipid measurements in the metabolomics analysis adjusted for body mass index (BMI). Galectin-1 was associated with several measurements of adiposity, insulin secretion and insulin sensitivity, while galectin-3 was associated with triglyceride-glucose index (TyG) and fasting insulin levels. Both galectins were associated with inflammatory pathways and fatty acid binding protein (FABP)4 and -5-regulated triglyceride metabolic pathways. Galectin-1 was also associated with several proteins related to adipose tissue differentiation.

Conclusions The association profiles for galectin-1 and galectin-3 indicate overlapping metabolic effects in humans, while the distinctly different associations seen with fat mass, fat distribution, and adipose tissue differentiation markers may suggest a functional role of galectin-1 in obesity.

BACKGROUND: Fatty acids may influence lean tissue volume and skeletal muscle function. We previously reported in young lean participants that overfeeding polyunsaturated fat (PUFA) compared with saturated fat (SFA) induced greater lean tissue accumulation despite similar weight gain.

OBJECTIVE: In a double-blind randomized controlled trial (RCT), we aimed to investigate if the differential effects of overfeeding SFA and PUFA on lean tissue accumulation could be replicated in individuals with overweight, and identify potential determinants. Further, using substitution models, we investigated associations between SFA and PUFA levels with lean tissue volume, in a large population-based sample (UK Biobank).

METHODS: Sixty-one males and females with overweight (BMI 27.3 (interquartile range 25.4 to 29.3), age 43 (interquartile range 36 to 48)) were overfed SFA (palm oil) or n-6 PUFA (sunflower oil) for 8 weeks. Lean tissue was assessed by magnetic resonance imaging (MRI). We had access to n=13849 participants with data on diet, covariates and MRI measurements of lean tissue, as well as 9119 participants with data on circulating fatty acids, in the UK Biobank.

RESULTS: Body weight gain (mean±SD) was similar in PUFA (2.01±1.90 kg) and SFA (2.31±1.38 kg) groups. Lean tissue increased to a similar extent (0.54±0.93 L and 0.67±1.21 L for PUFA and SFA group, respectively, with a difference between groups of 0.07 (-0,21, 0,35)). We observed no differential effects on circulating amino acids, myostatin or interleukin-15 and no clear determinants of lean tissue accumulation. Similar non-significant results for SFA and PUFA were observed in UK Biobank, but circulating fatty acids demonstrated ambiguous and sex-dependent associations.

CONCLUSION: Overfeeding SFA or PUFA does not differentially affect lean tissue accumulation during 8 weeks in individuals with overweight. A lack of dietary fat type-specific effects on lean tissue is supported by specified substitution models in a large population-based cohort consuming their habitual diet.

TRIAL REGISTRATION: ClinicalTrials.gov Identifier: NCT02211612.

Early and accurate prediction of clinical outcomes holds great potential for patient prognostics and personalized treatment planning. Development of automated methods for estimation of medical image-based clinical parameters (e.g. total metabolic tumor volume, TMTV) could pave the way for predicting advanced clinical outcomes not explicitly available in the images, such as overall survival. We developed an automated framework that extracted tissue-wise multi-channel 2D projections from whole-body FDG-PET/CT volumes, by separating tissues based on CT Hounsfield units, and used a DenseNet-121 to estimate the TMTV from the projections. For transparency and interpretability, an image registration-based cohort saliency analysis was proposed. The network was applied on the autoPET cohort (501 scans representing lymphoma, lung cancer, melanoma) and evaluated using a single channel method (baseline) and a multi-channel method (proposed), for the purpose of comparison. The incorporation of multiple channels demonstrated an advantage in the TMTV prediction, outperforming the baseline model with a ΔMAE = –14.34 ml; ΔR2 = 0.1584; ΔICC = 0.1316 (p-value = 0.0098). The Pearson correlation coefficient (r) was computed between the ground truth (GT) tumor projections and the aggregated saliency maps. Statistical comparison, via bootstrapping, showed that the proposed model consistently outperformed the baseline, with significantly higher r across all cancer types and both sexes, except for melanoma in females. This implied that the aggregated saliency maps generated by the proposed model showed higher correspondence with the GT, compared to the baseline model. Our approach offers a promising and interpretable framework for the automated prediction of TMTV, with further potential to also predict advanced clinical outcomes.