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Brain metastases are common in patients with advanced melanoma and constitute a major cause of morbidity and mortality. Between 40% and 60% of melanomas harbor BRAF mutations. Selective BRAF inhibitor therapy has yielded improvement in clinical outcome; however, genetic discordance between the primary lesion and the metastatic tumor has been shown to occur. Currently, the only way to characterize the genetic landscape of a brain metastasis is by tissue sampling, which carries risks and potential complications. The aim of this study was to investigate the use of radiomics analysis for non-invasive identification of BRAF mutation in patients with melanoma brain metastases, based on conventional magnetic resonance imaging (MRI) data. We applied a machine-learning method, based on MRI radiomics features for noninvasive characterization of the BRAF status of brain metastases from melanoma (BMM) and applied it to BMM patients from two tertiary neuro-oncological centers. All patients underwent surgical resection for BMM, and their BRAF mutation status was determined as part of their oncological work-up. Their routine preoperative MRI study was used for radiomics-based analysis in which 195 features were extracted and classified according to their BRAF status via a support vector machine. The BRAF status of 53 study patients, with 54 brain metastases (25 positive, 29 negative for BRAF mutation) was predicted with mean accuracy = 0.79 ± 0.13, mean precision = 0.77 ± 0.14, mean sensitivity = 0.72 ± 0.20, mean specificity = 0.83 ± 0.11 and with a 0.78 area under the receiver operating characteristic curve for positive BRAF mutation prediction. Radiomics-based noninvasive genetic characterization is feasible and should be further verified using large prospective cohorts.

In recent years, several automatic methods have been proposed to differentiate between brain tumor recurrence and treatment-related changes following radiotherapy, based on conventional and advanced MRI methods. Vascular parameters extracted from dynamic contrast-enhanced (DCE) were suggested as potential markers for disease progression. Yet, most studies that proposed machine-learning methods or relayed on threshold values of DCE for classification, did not analyze separately Glioblastoma (GBM) and brain metastasis, and often offer the same method/threshold values, neglecting the different vascularity of these two tumor types. Understanding and quantifying the differences between tumors type can improve early diagnosis of recurrence and may improve the reliability of identifying treatment-related changes. The aim of this study was to quantify vascular parameters of tumor recurrence and to assess differences between GBM and brain metastasis, specifically metastasis of breast cancer. METHOD: 41 MRI scans were included: 24 from patients with GBM: 20 with recurrence and 4 with treatment-related changes, and 17 scans from patients with brain metastasis of breast cancer: 10 with recurrence and 7 with treatment-related changes (diagnosis were based on radiological follow-up/histopathology, when available). MRIs were performed on a 3.0 Tesla MRI scanner (Siemens MAGNETOM Prisma) and included T1, T1+contrast, and DCE images. DCE analysis was performed using DUSTER and Ktrans, Vp and Kep pharmacokinetic parameters were compared between groups. RESULTS: Patients with GBM often demonstrated both tumor recurrence and areas with treatment-related changes. Significantly differences were detected between tumor recurrence and treatment-related changes, and higher Vp and Ktrans were detected in recurrent GBM as compared to recurrent breast cancer metastasis. Cutoff for differentiation between tumor recurrence and treatment-related changes will be presented specifically for each tumor type. To conclude, when defining quantitative threshold values for clinical decision-making, the inherent differences between primary and metastatic brain tumors should be taken into account.

Timely, accurate and reliable assessment of fetal brain development is essential to reduce short and long-term risks to fetus and mother. Fetal MRI is increasingly used for fetal brain assessment. Three key biometric linear measurements important for fetal brain evaluation are Cerebral Biparietal Diameter (CBD), Bone Biparietal Diameter (BBD), and Trans-Cerebellum Diameter (TCD), obtained manually by expert radiologists on reference slices, which is time consuming and prone to human error. The aim of this study was to develop a fully automatic method computing the CBD, BBD and TCD measurements from fetal brain MRI. The input is fetal brain MRI volumes which may include the fetal body and the mother's abdomen. The outputs are the measurement values and reference slices on which the measurements were computed. The method, which follows the manual measurements principle, consists of five stages: 1) computation of a Region Of Interest that includes the fetal brain with an anisotropic 3D U-Net classifier; 2) reference slice selection with a Convolutional Neural Network; 3) slice-wise fetal brain structures segmentation with a multiclass U-Net classifier; 4) computation of the fetal brain midsagittal line and fetal brain orientation, and; 5) computation of the measurements. Experimental results on 214 volumes for CBD, BBD and TCD measurements yielded a mean $L_1$ difference of 1.55mm, 1.45mm and 1.23mm respectively, and a Bland-Altman 95% confidence interval ($CI_{95}$) of 3.92mm, 3.98mm and 2.25mm respectively. These results are similar to the manual inter-observer variability. The proposed automatic method for computing biometric linear measurements of the fetal brain from MR imaging achieves human level performance. It has the potential of being a useful method for the assessment of fetal brain biometry in normal and pathological cases, and of improving routine clinical practice., 15 pages, 8 figures, presented in CARS 2020, submitted to IJCARS

A novel coronavirus termed severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a new strain of coronavirus causing coronavirus disease 2019 (COVID-19) disease, which emerged as a global pandemic. Data regarding the implications of COVID-19 disease at early gestation on fetal and obstetric outcomes is scarce. Thus, our aim was to investigate the effect of first and second trimester maternal COVID-19 disease on fetal and perinatal outcomes. This was a prospective cohort study of pregnant women with a laboratory-proven SARS-COV-2 infection contracted prior to 26 weeks gestation. Women were followed at a single tertiary medical center by serial sonographic examinations every 4–6 weeks to assess fetal well-being, growth, placental function, anatomic evaluation and signs of fetal infection. Amniocentesis was offered to assess amniotic fluid SARS-COV-2-PCR (polymerase chain reaction) and fetal brain magnetic resonance imaging (MRI) was offered at 30–32 weeks gestation. Demographic, obstetric and neonatal data were collected from history intake, medical charts or by telephone survey. Perinatal outcomes were compared between women infected at first vs. second trimester. 55 women with documented COVID-19 disease at early gestation were included and followed at our center. The mean maternal age was 29.6 ± 6.2 years and the mean gestational age at viral infection was 14.2 ± 6.7 weeks with 28 (51%) women infected at the first trimester and 27 (49%) at the second trimester. All patients but one experienced asymptomatic to mild symptoms. Of 22 patients who underwent amniocentesis, none had evidence of vertical transmission. None of the fetuses exhibited signs of central nervous system (CNS) disease, growth restriction and placental dysfunction on serial ultrasound examinations and fetal MRI. Pregnancies resulted in perinatal survival of 100% to date with mean gestational age at delivery of 38.6 ± 3.0 weeks and preterm birth &lt, 37 weeks rate of 3.4%. The mean birthweight was 3260 ± 411 g with no cases of small for gestational age infants. The obstetric and neonatal outcomes were similar among first vs. second trimester infection groups. We conclude SARS-CoV-2 infection at early gestation was not associated with vertical transmission and resulted in favorable obstetric and neonatal outcomes.

Objective T1-weighted MRI images are commonly used for volumetric assessment of brain structures. Magnetization prepared 2 rapid gradient echo (MP2RAGE) sequence offers superior gray (GM) and white matter (WM) contrast. This study aimed to quantitatively assess the agreement of whole brain tissue and deep GM (DGM) volumes obtained from MP2RAGE compared to the widely used MP-RAGE sequence. Methods Twenty-nine healthy participants were included in this study. All subjects underwent a 3T MRI scan acquiring high-resolution 3D MP-RAGE and MP2RAGE images. Twelve participants were re-scanned after one year. The whole brain, as well as DGM segmentation, was performed using CAT12, volBrain, and FSL-FAST automatic segmentation tools based on the acquired images. Finally, contrast-to-noise ratio between WM and GM (CNRWG), the agreement between the obtained tissue volumes, as well as scan-rescan variability of both sequences were explored. Results Significantly higher CNRWG was detected in MP2RAGE vs. MP-RAGE (Mean ± SD = 0.97 ± 0.04 vs. 0.8 ± 0.1 respectively; pp Conclusions Due to its higher CNR, MP2RAGE resulted in reproducible brain tissue segmentation, and thus is a recommended method for volumetric imaging biomarkers for the monitoring of neurological diseases.

INTRODUCTION: Peripheral neurofibromas (NF) represent one of the most common clinical presentations of the NF1. About 10% of patients with NF1 develop malignant peripheral nerve-sheath tumours (MPNST), which is a major cause of morbidity in NF1 patients. Improved prognosis can be achieved if MPNST are diagnosed at an early stage permitting ablative surgery with wide resection margins. Better understanding regarding the natural history and biology of MPNST has been obtained in the last decade, however existing imaging modalities are still imperfect for diagnosis of MPNST. The aim of this study was to use a machine learning method in order to differentiate between benign plexiform neurofibromas from MPNST in patients with NF1 based on conventional MRI methods. METHODS: Among 23 NF1 patients with pathologically-defined MPNST treated in the last decade in TASMC, seven patients with available preoperative MRI studies were included in the study. For comparison, 7 patients with benign plexiform neurofibromas were chosen from the database of the Gilbert Israeli Neurofibromatosis Center. Regions of interest at tumor areas were extracted from the post contrast 2D T(1)-weighted spin-echo images acquired on 1.5/3T MRI systems. For each patient, speeded-up-robust (SURF) features were extracted from lesion areas using Matlab bagOfFeatures algorithm. The algorithm was trained with number of visual words =500, grid point selection method, strongest features=0.8 and grid step size (in pixels)=[5 5]. The algorithm was tested with 100 iterations, with the data being randomly split into training (80%) and testing (20%) datasets. RESULTS: A code-book of lesion representations was generated for each group and was given as input to the support-vector-machine (SVM) classifier. Classification results achieved mean accuracy=80%, mean sensitivity=72% and mean specificity=87%. CONCLUSION: Our preliminary results suggest that a quantitative image representation method based on Bag-of-Features may assist in the classification between benign plexiform neurofibromas and MPNST in NF1.

INTRODUCTION: High grade brain tumors and brain metastasis are the two most common malignant brain tumors in adults. There is high clinical importance for differentiating between the two, as well as for sub-classification of metastases based on their origin, since medical staging, surgical planning, and therapeutic decisions are vastly different for each tumor type. MRI is the modality of choice for the assessment of patients with brain tumors. However, in many cases, differentiation between the tumor types may be challenging due to their similar appearance on MRI. The aim of this study was to develop an automatic method for segmentation and classification of brain tumors based on conventional MRI. METHODS: A retrospective analysis of 439 MRI data sets: 212 from patients with high grade glioma and 227 from patients with brain metastasis of breast, lung and other origins, was performed. Data include post contrast 3D T1 weighted images acquired at 1.5/3.0 Tesla MRI and clinical information including age, weight and gender. Following preprocessing and automatic segmentation of the enhancing tumor, several features were extracted: bag of features, first and second order statistical, morphological and wavelet features. The classifiers were trained in a 5-fold manner on 80% of the dataset and tested on the remaining 20% of the dataset. Sensitivity, specificity and accuracy were calculated. RESULTS: Best classification results obtained when classifying between high grade brain tumors and brain metastasis, using quadratic SVM classifier with mean accuracy=0.85, sensitivity=0.82 and specificity=0.87. Classifying brain metastases by their origin achieved best results with cosin KNN classifier with mean specificity=0.85 but low sensitivity=0.46. CONCLUSION: The proposed study demonstrates the use of machine learning methods for automatic segmentation and classification of brain tumors based on conventional MRI. Such methods may have great clinical importance in assisting in the diagnosis of new patients.

We report on a series of nine patients with high grade glioma (HGG) who developed punctate contrast-enhancing foci on MRI, several years after tumor treatment. The lesions were considered suspicious for tumor progression at their onset, but were proven over time to be benign in nature. Five of the patients were treated for glioblastoma; 3 for anaplastic astrocytoma, and one for anaplastic oligodendroglioma. Median age of the patients at diagnosis was 48 years (31–62 years). The mean time of onset of “suspicious spots” following treatment was 51 months after diagnosis (6–170 months). Of note, median overall survival of the group is 96 months; with none of the nine patients deceased, and only 2 patients known to have progression. The remainder of the 7 patients are free of active tumor and being followed off treatment. No apparent clinical manifestations of the lesions have been observed. The majority of the punctate lesions were of a dynamic nature; they appeared and spontaneously disappeared over time, without intervention. All lesions developed within the field of radiation treatment; some were contralateral to the tumor. Radiological characteristics of the lesions including analysis of SWI, DWI, and DCE magnetic resonance imaging sequences will be reported. Punctate enhancing foci in the region of radiation treatment are phenomena which may be manifestation of delayed treatment sequelae in HGG. Patients with these lesions are on average of younger age and have a particularly long survival. These lesions appeared several years following oncological treatment. The foci should be initially followed more closely until shown to be of a non-progressive nature; they should not be presumed to be indicative of tumor recurrence. Possible etiology of the lesions may be related to vascular changes induced by radiation.

Neuroplasticity studies examining children with hemiparesis (CH) have focused predominantly on unilateral interventions. CH also have bimanual coordination impairments with bimanual interventions showing benefits. We explored neuroplasticity following hand-arm bimanual intensive therapy (HABIT) of 60 hours in twelve CH (6 females, mean age 11 ± 3.6 y). Serial behavioral evaluations and MR imaging including diffusion tensor (DTI) and functional (fMRI) imaging were performed before, immediately after, and at 6-week follow-up. Manual skills were assessed repeatedly with the Assisting Hand Assessment, Children’s Hand Experience Questionnaire, and Jebsen-Taylor Test of Hand Function. Beta values, indicating the level of activation, and lateralization index (LI), indicating the pattern of brain activation, were computed from fMRI. White matter integrity of major fibers was assessed using DTI. 11/12 children showed improvement after intervention in at least one measure, with 8/12 improving on two or more tests. Changes were retained in 6/8 children at follow-up. Beta activation in the affected hemisphere increased at follow-up, and LI increased both after intervention and at follow-up. Correlations between LI and motor function emerged after intervention. Increased white matter integrity was detected in the corpus callosum and corticospinal tract after intervention in about half of the participants. Results provide first evidence for neuroplasticity changes following bimanual intervention in CH. This project was funded by grants from Guy’s and St Thomas’ Charity, Marnie Kimelman Trust and ILAN, the Israeli Association for Disabled children. Beit Issie Shapiro funded and provided the camp venue. D. Green was supported by a grant from the Department of Immigration and Absorption during 2010-2011.

Blood oxygenation level dependence (BOLD) imaging under either hypercapnia or hyperoxia has been used to study neuronal activation and for assessment of various brain pathologies. We evaluated the benefit of a combined protocol of BOLD imaging during both hyperoxic and hypercapnic challenges (termed hemodynamic response imaging (HRI)). Nineteen healthy controls and seven patients with primary brain tumors were included: six with glioblastoma (two newly diagnosed and four with recurrent tumors) and one with atypical-meningioma. Maps of percent signal intensity changes (ΔS) during hyperoxia (carbogen; 95%O2+5%CO2) and hypercapnia (95%air+5%CO2) challenges and vascular reactivity mismatch maps (VRM; voxels that responded to carbogen with reduced/absent response to CO2) were calculated. VRM values were measured in white matter (WM) and gray matter (GM) areas of healthy subjects and used as threshold values in patients. Significantly higher response to carbogen was detected in healthy subjects, compared to hypercapnia, with a GM/WM ratio of 3.8 during both challenges. In patients with newly diagnosed/treatment-naive tumors (n = 3), increased response to carbogen was detected with substantially increased VRM response (compared to threshold values) within and around the tumors. In patients with recurrent tumors, reduced/absent response during both challenges was demonstrated. An additional finding in 2 of 4 patients with recurrent glioblastoma was a negative response during carbogen, distant from tumor location, which may indicate steal effect. In conclusion, the HRI method enables the assessment of blood vessel functionality and reactivity. Reference values from healthy subjects are presented and preliminary results demonstrate the potential of this method to complement perfusion imaging for the detection and follow up of angiogenesis in patients with brain tumors.

The neonatal period is considered to be essential for neurodevelopment and wellbeing throughout the life span, yet little is known about brain–behavior relationships in the neonatal period. The aim of this study was to evaluate the association between neonatal sensory–motor regulation and white-matter (WM) integrity of major fiber tracts in the neonatal period. We hypothesized that WM integrity of sensory–motor systems would predict neurobehavioral maturation during the first month of life.Forty-nine premature neonates underwent magnetic-resonance-imaging at term. Diffusion-tensor-imaging analysis was performed in major WM tracts along with repeated neonatal neurobehavioral evaluations assessing sensory reactivity and motor regulation.Difficulties in one or more behavioral sub-category, mostly in auditory and visual attention, hypotonicity and jitteriness, were documented in 78.3% infants at term. Sixty-six percent of infants experienced difficulties, mostly in auditory attention, head–neck control, hypotonicity and motor asymmetry, at 44 weeks.Attention difficulties were associated with reduced integrity of cerebral and superior cerebellar peduncles; while tonicity was associated with reduced integrity of the corpus-callosum and inferior–posterior tracts. Overall, results showed that early maturing tracts were related with the degree of typicality of sensory reactivity status while late maturing tracts were related with the degree of typicality of tonic regulation. WM integrity and maturation factors explained 40.2% of the variance in neurobehavior at 44 weeks.This study suggests that in preterm neonates, deviant sensory–motor reactivity can be detected very early in development in manners that are related to lower integrity/maturational level of early and late maturing fiber tracts.

Can brain activity reveal a covert choice? Making a choice often evokes distinct emotions that accompany decision processes. Amygdala has been implicated in choice behavior that is guided by a prospective negative outcome. However, its specific involvement in emotional versus cognitive processing of choice behavior has been a subject of controversy. In this study, the human amygdala was monitored by functional magnetic resonance imaging (fMRI) while subjects were playing in a naturalistic choice paradigm against the experimenter. In order to win, players had to occasionally choose to bluff their opponent, risk “getting caught,” and suffer a loss. A critical period, when choice has been made but outcome was still unknown, activated the amygdala preferentially following the choice that entailed risk of loss. Thus, the response of the amygdala differentiated between subject's covert choice of either playing fair or foul. These results support a role of the amygdala in choice behavior, both in the appraisal of inherent value of choice and the signaling of prospective negative outcomes.