Autism Spectrum Disorder (ASD) affects approximately 1% of the population and leads to impairments in social interaction, communication and restricted, repetitive behaviours. Establishing robust ...neuroimaging biomarkers of ASD using structural magnetic resonance imaging (MRI) is an important step for diagnosing and tailoring treatment, particularly early in life when interventions can have the greatest effect. However currently, there is mixed findings on the structural brain changes associated with autism. Therefore in this systematic review, recent (post-2007), high-resolution (3 T) MRI studies investigating brain morphology associated with ASD have been collated to identify robust neuroimaging biomarkers of ASD. A systematic search was conducted on three databases; PubMed, Web of Science and Scopus, resulting in 123 reviewed articles. Patients with ASD were observed to have increased whole brain volume, particularly under 6 years of age. Other consistent changes observed in ASD patients include increased volume in the frontal and temporal lobes, increased cortical thickness in the frontal lobe, increased surface area and cortical gyrification, and increased cerebrospinal fluid volume, as well as reduced cerebellum volume and reduced corpus callosum volume, compared to typically developing controls. Findings were inconsistent regarding the developmental trajectory of brain volume and cortical thinning with age in ASD, as well as potential volume differences in the white matter, hippocampus, amygdala, thalamus and basal ganglia. To elucidate these inconsistencies, future studies should look towards aggregating MRI data from multiple sites or available repositories to avoid underpowered studies, as well as utilising methods which quantify larger-scale image features to reduce the number of statistical tests performed, and hence risk of false positive findings. Additionally, studies should look to perform a thorough validation strategy, to ensure generalisability of study findings, as well as look to leverage the improved image resolution of 3 T scanning to identify subtle brain changes related to ASD.
Cerebral palsy is a childhood-onset, lifelong neurological disorder that primarily impairs motor function. Unilateral cerebral palsy (UCP), which impairs use of one hand and perturbs bimanual ...co-ordination, is the most common form of the condition. The main contemporary upper limb rehabilitation strategies for UCP are constraint-induced movement therapy and bimanual intensive therapy. In this Review, we outline the factors that are crucial to the success of motor rehabilitation in children with UCP, including the dose of training, the relevance of training to daily life, the suitability of training to the age and goals of the child, and the ability of the child to maintain close attention to the tasks. Emerging evidence suggests that the first 2 years of life are a critical period during which interventions for UCP could be more effective than in later life. Abnormal brain organization in UCP, and the effects of development on rehabilitation, must also be understood to develop new effective interventions. Therefore, we also consider neuroimaging methods that can provide insight into the neurobiology of UCP and how the condition responds to existing therapies. We discuss how these methods could shape future rehabilitative strategies based on the neurobiology of UCP and the therapy-induced changes seen in the brain.
The deep grey matter (DGM) nuclei of the brain play a crucial role in learning, behaviour, cognition, movement and memory. Although automated segmentation strategies can provide insight into the ...impact of multiple neurological conditions affecting these structures, such as Multiple Sclerosis (MS), Huntington’s disease (HD), Alzheimer’s disease (AD), Parkinson’s disease (PD) and Cerebral Palsy (CP), there are a number of technical challenges limiting an accurate automated segmentation of the DGM. Namely, the insufficient contrast of T1 sequences to completely identify the boundaries of these structures, as well as the presence of iso-intense white matter lesions or extensive tissue loss caused by brain injury. Therefore in this systematic review, 269 eligible studies were analysed and compared to determine the optimal approaches for addressing these technical challenges. The automated approaches used among the reviewed studies fall into three broad categories, atlas-based approaches focusing on the accurate alignment of atlas priors, algorithmic approaches which utilise intensity information to a greater extent, and learning-based approaches that require an annotated training set. Studies that utilise freely available software packages such as FIRST, FreeSurfer and LesionTOADS were also eligible, and their performance compared. Overall, deep learning approaches achieved the best overall performance, however these strategies are currently hampered by the lack of large-scale annotated data. Improving model generalisability to new datasets could be achieved in future studies with data augmentation and transfer learning. Multi-atlas approaches provided the second-best performance overall, and may be utilised to construct a “silver standard” annotated training set for deep learning. To address the technical challenges, providing robustness to injury can be improved by using multiple channels, highly elastic diffeomorphic transformations such as LDDMM, and by following atlas-based approaches with an intensity driven refinement of the segmentation, which has been done with the Expectation Maximisation (EM) and level sets methods. Accounting for potential lesions should be achieved with a separate lesion segmentation approach, as in LesionTOADS. Finally, to address the issue of limited contrast, R2*, T2* and QSM sequences could be used to better highlight the DGM due to its higher iron content. Future studies could look to additionally acquire these sequences by retaining the phase information from standard structural scans, or alternatively acquiring these sequences for only a training set, allowing models to learn the “improved” segmentation from T1-sequences alone.
•We present a comprehensive review of deep grey matter segmentation methods.•We detail each of the methodologies and software, and compare their performance.•We outline the advantages and limitations of the different approaches.•We discuss future directions to account for brain injury and dataset limitations.
The Ovarian-Adnexal Reporting and Data System (O-RADS) US risk stratification and management system is designed to provide consistent interpretations, to decrease or eliminate ambiguity in US reports ...resulting in a higher probability of accuracy in assigning risk of malignancy to ovarian and other adnexal masses, and to provide a management recommendation for each risk category. It was developed by an international multidisciplinary committee sponsored by the American College of Radiology and applies the standardized reporting tool for US based on the 2018 published lexicon of the O-RADS US working group. For risk stratification, the O-RADS US system recommends six categories (O-RADS 0-5), incorporating the range of normal to high risk of malignancy. This unique system represents a collaboration between the pattern-based approach commonly used in North America and the widely used, European-based, algorithmic-style International Ovarian Tumor Analysis (IOTA) Assessment of Different Neoplasias in the Adnexa model system, a risk prediction model that has undergone successful prospective and external validation. The pattern approach relies on a subgroup of the most predictive descriptors in the lexicon based on a retrospective review of evidence prospectively obtained in the IOTA phase 1-3 prospective studies and other supporting studies that assist in differentiating management schemes in a variety of almost certainly benign lesions. With O-RADS US working group consensus, guidelines for management in the different risk categories are proposed. Both systems have been stratified to reach the same risk categories and management strategies regardless of which is initially used. At this time, O-RADS US is the only lexicon and classification system that encompasses all risk categories with their associated management schemes.
Preterm birth imposes a high risk for developing neuromotor delay. Earlier prediction of adverse outcome in preterm infants is crucial for referral to earlier intervention. This study aimed to ...predict abnormal motor outcome at 2 years from early brain diffusion magnetic resonance imaging (MRI) acquired between 29 and 35 weeks postmenstrual age (PMA) using a deep learning convolutional neural network (CNN) model.
Seventy-seven very preterm infants (born <31 weeks gestational age (GA)) in a prospective longitudinal cohort underwent diffusion MR imaging (3T Siemens Trio; 64 directions, b = 2000 s/mm2). Motor outcome at 2 years corrected age (CA) was measured by Neuro-Sensory Motor Developmental Assessment (NSMDA). Scores were dichotomised into normal (functional score: 0, normal; n = 48) and abnormal scores (functional score: 1–5, mild-profound; n = 29). MRIs were pre-processed to reduce artefacts, upsampled to 1.25 mm isotropic resolution and maps of fractional anisotropy (FA) were estimated. Patches extracted from each image were used as inputs to train a CNN, wherein each image patch predicted either normal or abnormal outcome. In a postprocessing step, an image was classified as predicting abnormal outcome if at least 27% (determined by a grid search to maximise the model performance) of its patches predicted abnormal outcome. Otherwise, it was considered as normal. Ten-fold cross-validation was used to estimate performance. Finally, heatmaps of model predictions for patches in abnormal scans were generated to explore the locations associated with abnormal outcome.
For the identification of infants with abnormal motor outcome based on the FA data from early MRI, we achieved mean sensitivity 70% (standard deviation SD 19%), mean specificity 74% (SD 39%), mean AUC (area under the receiver operating characteristic curve) 72% (SD 14%), mean F1 score of 68% (SD 13%) and mean accuracy 73% (SD 19%) on an unseen test data set. Patch-based prediction heatmaps showed that the patches around the motor cortex and somatosensory regions were most frequently identified by the model with high precision (74%) as a location associated with abnormal outcome. Part of the cerebellum, and occipital and frontal lobes were also highly associated with abnormal NSMDA/motor outcome.
This study established the potential of an early brain MRI-based deep learning CNN model to identify preterm infants at risk of a later motor impairment and to identify brain regions predictive of adverse outcome. Results suggest that predictions can be made from FA maps of diffusion MRIs well before term equivalent age (TEA) without any prior knowledge of which MRI features to extract and associated feature extraction steps. This method, therefore, is suitable for any case of brain condition/abnormality. Future studies should be conducted on a larger cohort to re-validate the robustness and effectiveness of these models.
•A CNN model is able to predict motor outcome from the brain diffusion MRI.•Motor cortex and somatosensory regions had the highest association with abnormality.•Significant implications for targeted early interventions in preterm infants.
Digital mammography combined with tomosynthesis is gaining clinical acceptance, but data are limited that show its impact in the clinical environment. We assessed the changes in performance measures, ...if any, after the introduction of tomosynthesis systems into our clinical practice.
In this observational study, we used verified practice- and outcome-related databases to compute and compare recall rates, biopsy rates, cancer detection rates, and positive predictive values for six radiologists who interpreted screening mammography studies without (n = 13,856) and with (n = 9499) the use of tomosynthesis. Two-sided analyses (significance declared at p < 0.05) accounting for reader variability, age of participants, and whether the examination in question was a baseline were performed.
For the group as a whole, the introduction and routine use of tomosynthesis resulted in significant observed changes in recall rates from 8.7% to 5.5% (p < 0.001), nonsignificant changes in biopsy rates from 15.2 to 13.5 per 1000 screenings (p = 0.59), and cancer detection rates from 4.0 to 5.4 per 1000 screenings (p = 0.18). The invasive cancer detection rate increased from 2.8 to 4.3 per 1000 screening examinations (p = 0.07). The positive predictive value for recalls increased from 4.7% to 10.1% (p < 0.001).
The introduction of breast tomosynthesis into our practice was associated with a significant reduction in recall rates and a simultaneous increase in breast cancer detection rates.
Physical activity plays a key role in cancer survivorship. The purpose of this investigation was to (a) describe the post-surgical physical activity trajectories of endometrial (n = 65) and ovarian ...(n = 31) cancer patients and (b) identify clinical and demographic predictors of physical activity over time.
96 participants wore an Actiwatch accelerometer for three days at each of three time points (one week, one month and four months) after surgical intervention for their endometrial or ovarian cancer diagnosis. Analyses were conducted using linear mixed effects regression modeling in SAS 9.4.
For both tumor types, although physical activity levels increased with time after surgery, even at four months patients were performing only a small fraction of the 150 minutes of recommended weekly moderate to vigorous physical activity. At 1 week, subjects were completing on average 14 minutes/week (SD = 4) of moderate-to-vigorous physical activity, compared to 14 minutes/week (SD = 2) of moderate-to-vigorous physical activity at four months post-surgery (p < .05). Better self-rated health was associated with higher physical activity (p = 0.02) in endometrial cancer survivors only. BMI, age, surgery type and use of neoadjuvant chemotherapy were not associated with activity over time.
Our findings suggest that physical activity levels are different for those with better self-rated health, but those individuals are still insufficiently active. This study adds new information describing the trajectories and variables that influence physical activity in gynecologic cancer survivors after surgery and highlights the need for health promotion interventions in this population.
To perform an updated Markov modeling to assess the optimal age for bilateral salpingo-oophorectomy (BSO) at the time of hysterectomy for benign indication.
We performed a literature review that ...assessed hazard ratios (HRs) for mortality by disease, age, hysterectomy with or without BSO, and estrogen therapy use. Base mortality rates were derived from national vital statistics data. A Markov model from reported HRs predicted the proportion of the population staying alive to age 80 years by 1-year and 5-year age groups at time of surgery, from age 45 to 55 years. Those younger than age 50 years were modeled as either taking postoperative estrogen or not; those 50 and older were modeled as not receiving estrogen. Computations were performed with R 3.5.1, using Bayesian integration for HR uncertainty.
Performing salpingo-oophorectomy before age 50 years for those not taking estrogen yields a lower survival proportion to age 80 years than hysterectomy alone before age 50 years (52.8% Bayesian CI 40.7-59.7 vs 63.5% Bayesian CI 62.2-64.9). At or after age 50 years, there were similar proportions of those living to age 80 years with hysterectomy alone (66.4%, Bayesian CI 65.0-67.6) compared with concurrent salpingo-oophorectomy (66.9%, Bayesian CI 64.4-69.0). Importantly, those taking estrogen when salpingo-oophorectomy was performed before age 50 years had similar proportions of cardiovascular disease, stroke, and people living to age 80 years as those undergoing hysterectomy alone or those undergoing hysterectomy and salpingo-oophorectomy at age 50 years and older.
This updated Markov model argues for the consideration of concurrent salpingo-oophorectomy for patients who are undergoing hysterectomy at age 50 and older and suggests that initiating estrogen in those who need salpingo-oophorectomy before age 50 years mitigates increased mortality risk.
Diffusion MRI (dMRI) tractography analyses are difficult to perform in the presence of brain pathology. Automated methods that rely on cortical parcellation for structural connectivity studies often ...fail, while manually defining regions is extremely time consuming and can introduce human error. Both methods also make assumptions about structure-function relationships that may not hold after cortical reorganisation. Seeding tractography with functional-MRI (fMRI) activation is an emerging method that reduces these confounds, but inherent smoothing of fMRI signal may result in the inclusion of irrelevant pathways. This paper describes a novel fMRI-seeded dMRI-analysis pipeline based on surface-meshes that reduces these issues and utilises machine-learning to generate task specific white matter pathways, minimising the requirement for manually-drawn ROIs. We directly compared this new strategy to a standard voxelwise fMRI-dMRI approach, by investigating correlations between clinical scores and dMRI metrics of thalamocortical and corticomotor tracts in 31 children with unilateral cerebral palsy. The surface-based approach successfully processed more participants (87%) than the voxel-based approach (65%), and provided significantly more-coherent tractography. Significant correlations between dMRI metrics and five clinical scores of function were found for the more superior regions of these tracts. These significant correlations were stronger and more frequently found with the surface-based method (15/20 investigated were significant; R2 = 0.43-0.73) than the voxelwise analysis (2 sig. correlations; 0.38 & 0.49). More restricted fMRI signal, better-constrained tractography, and the novel track-classification method all appeared to contribute toward these differences.
Mammography plays a key role in early breast cancer detection. Single-institution studies have shown that adding tomosynthesis to mammography increases cancer detection and reduces false-positive ...results.
To determine if mammography combined with tomosynthesis is associated with better performance of breast screening programs in the United States.
Retrospective analysis of screening performance metrics from 13 academic and nonacademic breast centers using mixed models adjusting for site as a random effect.
Period 1: digital mammography screening examinations 1 year before tomosynthesis implementation (start dates ranged from March 2010 to October 2011 through the date of tomosynthesis implementation); period 2: digital mammography plus tomosynthesis examinations from initiation of tomosynthesis screening (March 2011 to October 2012) through December 31, 2012.
Recall rate for additional imaging, cancer detection rate, and positive predictive values for recall and for biopsy.
A total of 454,850 examinations (n=281,187 digital mammography; n=173,663 digital mammography + tomosynthesis) were evaluated. With digital mammography, 29,726 patients were recalled and 5056 biopsies resulted in cancer diagnosis in 1207 patients (n=815 invasive; n=392 in situ). With digital mammography + tomosynthesis, 15,541 patients were recalled and 3285 biopsies resulted in cancer diagnosis in 950 patients (n=707 invasive; n=243 in situ). Model-adjusted rates per 1000 screens were as follows: for recall rate, 107 (95% CI, 89-124) with digital mammography vs 91 (95% CI, 73-108) with digital mammography + tomosynthesis; difference, -16 (95% CI, -18 to -14; P < .001); for biopsies, 18.1 (95% CI, 15.4-20.8) with digital mammography vs 19.3 (95% CI, 16.6-22.1) with digital mammography + tomosynthesis; difference, 1.3 (95% CI, 0.4-2.1; P = .004); for cancer detection, 4.2 (95% CI, 3.8-4.7) with digital mammography vs 5.4 (95% CI, 4.9-6.0) with digital mammography + tomosynthesis; difference, 1.2 (95% CI, 0.8-1.6; P < .001); and for invasive cancer detection, 2.9 (95% CI, 2.5-3.2) with digital mammography vs 4.1 (95% CI, 3.7-4.5) with digital mammography + tomosynthesis; difference, 1.2 (95% CI, 0.8-1.6; P < .001). The in situ cancer detection rate was 1.4 (95% CI, 1.2-1.6) per 1000 screens with both methods. Adding tomosynthesis was associated with an increase in the positive predictive value for recall from 4.3% to 6.4% (difference, 2.1%; 95% CI, 1.7%-2.5%; P < .001) and for biopsy from 24.2% to 29.2% (difference, 5.0%; 95% CI, 3.0%-7.0%; P < .001).
Addition of tomosynthesis to digital mammography was associated with a decrease in recall rate and an increase in cancer detection rate. Further studies are needed to assess the relationship to clinical outcomes.
PDF
