Preterm birth and subsequent intelligence and academic performance in youth: A cohort study.

INTRODUCTION: Worldwide, more children than before survive preterm birth. Preterm birth can affect long-term cognitive outcomes. The aim of our study was to investigate the association between preterm birth and academic performance and intelligence in youth. MATERIAL AND METHODS: This cohort study included all liveborn children in Denmark from 1978 to 2000. We used uni- and multivariable logistic and linear regressions to analyze associations between gestational age and school graduation, grade point average (GPA), attending conscription, and male intelligence scores at conscription. We adjusted for a priori defined potential confounders. RESULTS: The study included 1 450 681 children and found an association between preterm birth and lower academic performance, with children born extremely preterm having the lowest odds of graduating from lower- and upper secondary education (LSE and USE) and appearing before the conscription board (odds ratios of 0.45 [0.38-0.54], 0.52 [0.46-0.59], and 0.47 [0.39-0.56] for LSE, USE, and conscription, respectively, compared to the term group). Statistically significant differences were observed in LSE for total GPA and core subject GPA with higher GPAs in the term group, which were considered clinically relevant for mathematics with a 0.71 higher grade point for the term compared to the extremely preterm. Conversely, USE differences were less evident, and in linear regression models we found that preterm birth was associated with higher GPAs in the adjusted analyses; however, this was not statistically significant. We demonstrated statistically significant differences in intelligence scores at conscription with lower scores in the three preterm groups (-5.13, -2.73, and - 0.76, respectively) compared to the term group. CONCLUSIONS: Low gestational age at birth was associated with not graduating from LSE and USE, achieving lower GPAs in LSE, not attending conscription, and lower intelligence scores in young adulthood. The findings remained significant after adjusting for potential confounders.

What Genetics Can Do for Oncological Imaging: A Systematic Review of the Genetic Validation Data Used in Radiomics Studies.

(1) Background: Radiogenomics is motivated by the concept that biomedical images contain information that reflects underlying pathophysiology. This review focused on papers that used genetics to validate their radiomics models and outcomes and assess their contribution to this emerging field. (2) Methods: All original research with the words radiomics and genomics in English and performed in humans up to 31 January 2022, were identified on Medline and Embase. The quality of the studies was assessed with Radiomic Quality Score (RQS) and the Cochrane recommendation for diagnostic accuracy study Quality Assessment 2. (3) Results: 45 studies were included in our systematic review, and more than 50% were published in the last two years. The studies had a mean RQS of 12, and the studied tumors were very diverse. Up to 83% investigated the prognosis as the main outcome, with the rest focusing on response to treatment and risk assessment. Most applied either transcriptomics (54%) and/or genetics (35%) for genetic validation. (4) Conclusions: There is enough evidence to state that new science has emerged, focusing on establishing an association between radiological features and genomic/molecular expression to explain underlying disease mechanisms and enhance prognostic, risk assessment, and treatment response radiomics models in cancer patients.

Association of Apgar score at 5 minutes with academic performance and intelligence in youth: A cohort study.

INTRODUCTION: The Apgar score is routinely given at childbirth worldwide. A low Apgar score at 5 minutes is a strong predictor for neonatal death. Scores below 7 have been associated with higher risks of later neurologic disability. Few studies have assessed the association between Apgar score and school performance and intelligence. The existing literature points towards a possible association between Apgar score and later cognitive function, but the contradictions call for further investigation to fully understand the potential association. This study aimed to examine the possible association between Apgar score at 5 minutes and academic performance and intelligence in youth. MATERIAL AND METHODS: The study is a cohort study. The cohort consists of all Danish liveborn children in 1978-2000 (n = 1 450 681). Data regarding pregnancies, births, parents, school grades, and intelligence of the children were retrieved from different Danish registers. Multiple imputations were performed to avoid discarding data. After exclusion, the final cohort consisted of 1 005 241 children. Associations between Apgar score at 5 minutes and school graduation, grades and attendance, and intelligence scores from conscription were analyzed using univariate and multivariate logistic and linear regressions. RESULTS: No association was found between Apgar score and graduating primary school. Adjusted odds ratio (aOR) of graduating upper secondary education and attending conscription were significantly lower for children with scores below 7 compared with 7-10: graduating upper secondary education: Apgar 0-3: aOR 0.79 (95% CI 0.67-0.93), Apgar 4-6: aOR 0.86 (95% CI 0.81-0.93), attending conscription: Apgar 0-3: aOR 0.73 (95% CI 0.59-0.91), Apgar 4-6: aOR 0.73 (95% CI 0.66-0.80). The Apgar 4-6 group had significantly lower total mean primary school grade average: -0.13 (95% CI -0.21 to -0.054) and lower mean intelligence scores at conscription: -0.57 (95% CI -1.09 to -0.058). All other differences remained insignificant. CONCLUSIONS: Performances when graduating school and attending conscription were overall equal regardless of Apgar score at 5 minutes. Chances of graduating primary school were the same irrespective of the score, but chances of graduating upper secondary education and attending conscription were significantly lower with scores below 7. The results suggest that children with scores below 7 may fail to appear at upper secondary education and conscription, but if they do, they perform equally to anyone else.

AI-driven attenuation correction for brain PET/MRI: Clinical evaluation of a dementia cohort and importance of the training group size.

INTRODUCTION: Robust and reliable attenuation correction (AC) is a prerequisite for accurate quantification of activity concentration. In combined PET/MRI, AC is challenged by the lack of bone signal in the MRI from which the AC maps has to be derived. Deep learning-based image-to-image translation networks present itself as an optimal solution for MRI-derived AC (MR-AC). High robustness and generalizability of these networks are expected to be achieved through large training cohorts. In this study, we implemented an MR-AC method based on deep learning, and investigated how training cohort size, transfer learning, and MR input affected robustness, and subsequently evaluated the method in a clinical setup, with the overall aim to explore if this method could be implemented in clinical routine for PET/MRI examinations. METHODS: A total cohort of 1037 adult subjects from the Siemens Biograph mMR with two different software versions (VB20P and VE11P) was used. The software upgrade included updates to all MRI sequences. The impact of training group size was investigated by training a convolutional neural network (CNN) on an increasing training group size from 10 to 403. The ability to adapt to changes in the input images between software versions were evaluated using transfer learning from a large cohort to a smaller cohort, by varying training group size from 5 to 91 subjects. The impact of MRI sequence was evaluated by training three networks based on the Dixon VIBE sequence (DeepDixon), T1-weighted MPRAGE (DeepT1), and ultra-short echo time (UTE) sequence (DeepUTE). Blinded clinical evaluation relative to the reference low-dose CT (CT-AC) was performed for DeepDixon in 104 independent 2-[18F]fluoro-2-deoxy-d-glucose ([18F]FDG) PET patient studies performed for suspected neurodegenerative disorder using statistical surface projections. RESULTS: Robustness increased with group size in the training data set: 100 subjects were required to reduce the number of outliers compared to a state-of-the-art segmentation-based method, and a cohort >400 subjects further increased robustness in terms of reduced variation and number of outliers. When using transfer learning to adapt to changes in the MRI input, as few as five subjects were sufficient to minimize outliers. Full robustness was achieved at 20 subjects. Comparable robust and accurate results were obtained using all three types of MRI input with a bias below 1% relative to CT-AC in any brain region. The clinical PET evaluation using DeepDixon showed no clinically relevant differences compared to CT-AC. CONCLUSION: Deep learning based AC requires a large training cohort to achieve accurate and robust performance. Using transfer learning, only five subjects were needed to fine-tune the method to large changes to the input images. No clinically relevant differences were found compared to CT-AC, indicating that clinical implementation of our deep learning-based MR-AC method will be feasible across MRI system types using transfer learning and a limited number of subjects.

Impact of gestational age on child intelligence, attention and executive function at age 5: a cohort study.

OBJECTIVES: Preterm birth can affect cognition, but other factors including parental education and intelligence may also play a role, but few studies have adjusted for these potential confounders. We aimed to assess the impact of gestational age (GA), late preterm birth (34 to <37 weeks GA) and very to moderately preterm birth (<34 weeks GA) on intelligence, attention and executive function in a population of Danish children aged 5 years. DESIGN: Population-based prospective cohort study. SETTING: Denmark 2003-2008. PARTICIPANTS: A cohort of 1776 children and their mothers sampled from the Danish National Birth Cohort with information on GA, family and background factors and completed neuropsychological assessment at age 5. PRIMARY OUTCOME MEASURES: Wechsler Preschool and Primary Scale of Intelligence-Revised, Test of Everyday Attention for Children at Five and Behaviour Rating Inventory of Executive Function scores. RESULTS: For preterm birth <34 weeks GA (n=8), the mean difference in full-scale intelligence quotient(IQ) was -10.6 points (95% CI -19.4 to -1.8) when compared with the term group ≥37 weeks GA (n=1728), and adjusted for potential confounders. For the teacher-assessed Global Executive Composite, the mean difference was 5.3 points (95% CI 2.4 to 8.3) in the adjusted analysis, indicating more executive function difficulties in the preterm group <34 weeks GA compared with the term group. Maternal intelligence and parental education were weak confounders. No associations between late preterm birth 34 to <37 weeks GA (n=40) and poor cognition were shown. CONCLUSIONS: This study showed substantially lower intelligence and poorer executive function in children born <34 weeks GA compared with children born at term. GA may play an important role in determining cognitive abilities independent of maternal intelligence and parental education. Studies with larger sample sizes are needed to confirm these findings, as the proportion of children born preterm in this study population was small.