Pelvic PET/MR attenuation correction in the image space using deep learning

IntroductionThe five-class Dixon-based PET/MR attenuation correction (AC) model, which adds bone information to the four-class model by registering major bones from a bone atlas, has been shown to be error-prone. In this study, we introduce a novel method of accounting for bone in pelvic PET/MR AC by directly predicting the errors in the PET image space caused by the lack of bone in four-class Dixon-based attenuation correction.MethodsA convolutional neural network was trained to predict the four-class AC error map relative to CT-based attenuation correction. Dixon MR images and the four-class attenuation correction µ-map were used as input to the models. CT and PET/MR examinations for 22 patients ([18F]FDG) were used for training and validation, and 17 patients were used for testing (6 [18F]PSMA-1007 and 11 [68Ga]Ga-PSMA-11). A quantitative analysis of PSMA uptake using voxel- and lesion-based error metrics was used to assess performance.ResultsIn the voxel-based analysis, the proposed model reduced the median root mean squared percentage error from 12.1% and 8.6% for the four- and five-class Dixon-based AC methods, respectively, to 6.2%. The median absolute percentage error in the maximum standardized uptake value (SUVmax) in bone lesions improved from 20.0% and 7.0% for four- and five-class Dixon-based AC methods to 3.8%.ConclusionThe proposed method reduces the voxel-based error and SUVmax errors in bone lesions when compared to the four- and five-class Dixon-based AC models

Performance of magnetic resonance imaging-based prostate cancer risk calculators and decision strategies in two large European medical centres

Objectives: To compare the performance of currently available biopsy decision support tools incorporating magnetic resonance imaging (MRI) findings in predicting clinically significant prostate cancer (csPCa). Patients and Methods: We retrospectively included men who underwent prostate MRI and subsequent targeted and/or systematic prostate biopsies in two large European centres. Available decision support tools were identified by a PubMed search. Performance was assessed by calibration, discrimination, decision curve analysis (DCA) and numbers of biopsies avoided vs csPCa cases missed, before and after recalibration, at risk thresholds of 5%–20%. Results: A total of 940 men were included, 507 (54%) had csPCa. The median (interquartile range) age, prostate-specific antigen (PSA) level, and PSA density (PSAD) were 68 (63–72) years, 9 (7–15) ng/mL, and 0.20 (0.13–0.32) ng/mL2, respectively. In all, 18 multivariable risk calculators (MRI-RCs) and dichotomous biopsy decision strategies based on MRI findings and PSAD thresholds were assessed. The Van Leeuwen model and the Rotterdam Prostate Cancer Risk Calculator (RPCRC) had the best discriminative ability (area under the receiver operating characteristic curve 0.86) of the MRI-RCs that could be assessed in the whole cohort. DCA showed the highest clinical utility for the Van Leeuwen model, followed by the RPCRC. At the 10% threshold the Van Leeuwen model would avoid 22% of biopsies, missing 1.8% of csPCa, whilst the RPCRC would avoid 20% of biopsies, missing 2.6% of csPCas. These multivariable models outperformed all dichotomous decision strategies based only on MRI-findings and PSAD. Conclusions: Even in this high-risk cohort, biopsy decision support tools would avoid many prostate biopsies, whilst missing very few csPCa cases. The Van Leeuwen model had the highest clinical utility, followed by the RPCRC. These multivariable MRI-RCs outperformed and should be favoured over decision strategies based only on MRI and PSAD.</p

NMR-based metabolomics of biofluids in cancer

Author's accepted version (post-print).Available from 20/04/2019.acceptedVersio

Metabolomics identifies placental dysfunction and confirms Flt-1 (FMS-like tyrosine kinase receptor 1) biomarker specificity

Clinical end-stage parameters define the pregnancy disorders preeclampsia and fetal growth restriction while classification of the underlying placental dysfunction is missing and urgently needed. Flt-1 (FMS-like tyrosine kinase receptor 1) is the most promising placenta-derived predictive biomarker for preeclampsia. We aimed to classify placental dysfunction in preeclampsia and fetal growth restriction at delivery by metabolic profiling and authenticate the biomarker Flt-1 for placental dysfunction. We studied 143 pregnancies with or without preeclampsia and/or fetal growth restriction delivered by cesarean section. Metabolic placenta profiles were created by high-resolution magic angle spinning nuclear magnetic resonance spectroscopy and the resulting placental phenotypes obtained by hierarchical clustering. Placental Flt-1 expression (membrane-bound and soluble isoforms combined) and maternal serum Flt-1 expression (soluble isoforms) were analyzed by immunohistochemistry and ELISA, respectively. We identified 3 distinct placenta groups by 21 metabolites and diagnostic outcome parameters; normal placentas, moderate placental dysfunction, and severe placental dysfunction. Increased placental Flt-1 was associated with severe placental dysfunction, and increased serum Flt-1 was associated with moderate and severe placental dysfunction. The preeclamptic pregnancies with and without placental dysfunction could be distinguished by 5 metabolites and placental Flt-1. Placental Flt-1 alone could separate normal pregnancies with and without placental dysfunction. In conclusion, metabolomics could classify placental dysfunction and provide information not identified by traditional diagnostics and metabolites with biomarker potential were identified. Flt-1 was confirmed as precision biomarker for placental dysfunction, substantiating its usefulness for identification of high-risk pregnancies for preeclampsia and fetal growth restriction with placental involvement.acceptedVersio

Metabolic profiles of placenta in preeclampsia using HR-MAS MRS metabolomics

Introduction Preeclampsia is a heterogeneous gestational disease characterized by maternal hypertension and proteinuria, affecting 2–7% of pregnancies. The disorder is initiated by insufficient placental development, but studies characterizing the placental disease components are lacking. Methods Our aim was to phenotype the preeclamptic placenta using high-resolution magic angle spinning nuclear magnetic resonance spectroscopy (HR-MAS MRS). Placental samples collected after delivery from women with preeclampsia (n = 19) and normotensive pregnancies (n = 15) were analyzed for metabolic biomarkers including amino acids, osmolytes, and components of the energy and phospholipid metabolism. The metabolic biomarkers were correlated to clinical characteristics and inflammatory biomarkers in the maternal sera. Results Principal component analysis showed inherent differences in placental metabolic profiles between preeclamptic and normotensive pregnancies. Significant differences in metabolic profiles were found between placentas from severe and non-severe preeclampsia, but not between preeclamptic pregnancies with fetal growth restricted versus normal weight neonates. The placental metabolites correlated with the placental stress marker sFlt-1 and triglycerides in maternal serum, suggesting variation in placental stress signaling between different placental phenotypes. Discussion HR-MAS MRS is a sensitive method for defining the placental disease component of preeclampsia, identifying several altered metabolic pathways. Placental HR-MAS MRS analysis may improve insight into processes affected in the preeclamptic placenta, and represents a novel long-required tool for a sensitive placental phenotyping of this heterogeneous disease.acceptedVersio

Classification and biomarker identification of prostate tissue from TRAMP mice with hyperpolarized 13C-SIRA

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Associations of lipoprotein particle profile and objectively measured physical activity and sedentary time in schoolchildren: a prospective cohort study

Background: Our understanding of the mechanisms through which physical activity might benefit lipoprotein metabolism is inadequate. Here we characterise the continuous associations between physical activity of different intensities, sedentary time, and a comprehensive lipoprotein particle profile. Methods: Our cohort included 762 fifth grade (mean [SD] age = 10.0 [0.3] y) Norwegian schoolchildren (49.6% girls) measured on two separate occasions across one school year. We used targeted proton nuclear magnetic resonance (1H NMR) spectroscopy to produce 57 lipoprotein measures from fasted blood serum samples. The children wore accelerometers for seven consecutive days to record time spent in light-, moderate-, and vigorous-intensity physical activity, and sedentary time. We used separate multivariable linear regression models to analyse associations between the device-measured activity variables—modelled both prospectively (baseline value) and as change scores (follow-up minus baseline value)—and each lipoprotein measure at follow-up. Results: Higher baseline levels of moderate-intensity and vigorous-intensity physical activity were associated with a favourable lipoprotein particle profile at follow-up. The strongest associations were with the larger subclasses of triglyceride-rich lipoproteins. Sedentary time was associated with an unfavourable lipoprotein particle profile, the pattern of associations being the inverse of those in the moderate-intensity and vigorous-intensity physical activity analyses. The associations with light-intensity physical activity were more modest; those of the change models were weak. Conclusion: We provide evidence of a prospective association between time spent active or sedentary and lipoprotein metabolism in schoolchildren. Change in activity levels across the school year is of limited influence in our young, healthy cohort.publishedVersio

Associations of physical activity and sedentary time with lipoprotein subclasses in Norwegian schoolchildren: The Active Smarter Kids (ASK) study

Background and aims: Physical activity is favourably associated with certain markers of lipid metabolism. The relationship of physical activity with lipoprotein particle profiles in children is not known. Here we examine cross-sectional associations between objectively measured physical activity and sedentary time with serum markers of lipoprotein metabolism. Methods: Our cohort included 880 children (49.0% girls, mean age 10.2 years). Physical activity intensity and time spent sedentary were measured objectively using accelerometers. 30 measures of lipoprotein metabolism were quantified using nuclear magnetic resonance spectroscopy. Multiple linear regression models adjusted for age, sex, sexual maturity and socioeconomic status were used to determine associations of physical activity and sedentary time with lipoprotein measures. Additional models were adjusted for adiposity. Isotemporal substitution models quantified theoretical associations of replacing 30 min of sedentary time with 30 min of moderate- to vigorous-intensity physical activity (MVPA). Results: Time spent in MVPA was associated with a favourable lipoprotein profile independent of sedentary time. There were inverse associations with a number of lipoprotein measures, including most apolipoprotein B-containing lipoprotein subclasses and triglyceride measures, the ratio of total to high-density lipoprotein (HDL) cholesterol, and non-HDL cholesterol concentration. There were positive associations with larger HDL subclasses, HDL cholesterol concentration and particle size. Reallocating 30 min of sedentary time to MVPA had broadly similar associations. Sedentary time was only partly and weakly associated with an unfavourable lipoprotein profile. Conclusions: Physical activity of at least moderate-intensity is associated with a favourable lipoprotein profile in schoolchildren, independent of time spent sedentary, adiposity and other confounders.acceptedVersio

APIM-peptide targeting PCNA improves the efficacy of docetaxel treatment in the TRAMP mouse model of prostate cancer

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