SARS-CoV-2 variant-related abnormalities detected by prenatal MRI: a prospective case-control study

Background

There are known complications for fetuses after infection with SARS-CoV-2 during pregnancy. However, previous studies of SARS-CoV-2 in pregnancy have largely been limited to histopathologic studies of placentas and prenatal studies on the effects of different SARS-CoV-2 variants are scarce to date. To examine the effects of SARS-CoV-2 variants on the placenta and fetus, we investigated fetal and extra-fetal structures using prenatal MRI.

Methods

For this prospective case-control study, two obstetric centers consecutively referred pregnant women for prenatal MRI after confirmed SARS-CoV-2 infection. Thirty-eight prenatal MRI examinations were included after confirmed infection with SARS-CoV-2 and matched 1:1 with 38 control cases with respect to sex, MRI field strength, and gestational age (average deviation 1.76 ± 1.65, median 1.5 days). Where available, the pathohistological examination and vaccination status of the placenta was included in the analysis. In prenatal MRI, the shape and thickness of the placenta, possible lobulation, and vascular lesions were quantified. Fetuses were scanned for organ or brain abnormalities.

Findings

Of the 38 included cases after SARS-CoV-2 infection, 20/38 (52.6%) were infected with pre-Omicron variants and 18/38 (47.4%) with Omicron. Prenatal MRIs were performed on an average of 83 days (±42.9, median 80) days after the first positive PCR test. Both pre-Omicron (P = .008) and Omicron (P = .016) groups showed abnormalities in form of a globular placenta compared to control cases. In addition, placentas in the pre-Omicron group were significantly thickened (6.35, 95% CI .02-12.65, P = .048), and showed significantly more frequent lobules (P = .046), and hemorrhages (P = .002). Fetal growth restriction (FGR) was observed in 25% (n = 5/20, P = .017) in the pre-Omicron group.

Interpretation

SARS-CoV-2 infections in pregnancy can lead to placental lesions based on vascular events, which can be well visualized on prenatal MRI. Pre-Omicron variants cause greater damage than Omicron sub-lineages in this regard.

Funding

Vienna Science and Technology Fund.

Clinical Decision Support for Axillary Lymph Node Staging in Newly Diagnosed Breast Cancer Patients Based on 18F-FDG PET/MRI and Machine Learning

In addition to its high prognostic value, the involvement of axillary lymph nodes in breast cancer patients also plays an important role in therapy planning. Therefore, an imaging modality that can determine nodal status with high accuracy in patients with primary breast cancer is desirable. Our purpose was to investigate whether, in newly diagnosed breast cancer patients, machine-learning prediction models based on simple assessable imaging features on MRI or PET/MRI are able to determine nodal status with performance comparable to that of experienced radiologists; whether such models can be adjusted to achieve low rates of false-negatives such that invasive procedures might potentially be omitted; and whether a clinical framework for decision support based on simple imaging features can be derived from these models. Methods: Between August 2017 and September 2020, 303 participants from 3 centers prospectively underwent dedicated whole-body ¹⁸F-FDG PET/MRI. Imaging datasets were evaluated for axillary lymph node metastases based on morphologic and metabolic features. Predictive models were developed for MRI and PET/MRI separately using random forest classifiers on data from 2 centers and were tested on data from the third center. Results: The diagnostic accuracy for MRI features was 87.5% both for radiologists and for the machine-learning algorithm. For PET/MRI, the diagnostic accuracy was 89.3% for the radiologists and 91.2% for the machine-learning algorithm, with no significant differences in diagnostic performance between radiologists and the machine-learning algorithm for MRI (P = 0.671) or PET/MRI (P = 0.683). The most important lymph node feature was tracer uptake, followed by lymph node size. With an adjusted threshold, a sensitivity of 96.2% was achieved by the random forest classifier, whereas specificity, positive predictive value, negative predictive value, and accuracy were 68.2%, 78.1%, 93.8%, and 83.3%, respectively. A decision tree based on 3 simple imaging features could be established for MRI and PET/MRI. Conclusion: Applying a high-sensitivity threshold to the random forest results might potentially avoid invasive procedures such as sentinel lymph node biopsy in 68.2% of the patients.

[Requirements and value of interdisciplinary communication and consultation]

Interdisciplinary communication and consultation take up a relevant part of the radiological workload. They are essential for high-quality and ubiquitous medical care. There are different modalities of interdisciplinary communication, each with its own advantages and disadvantages. This article provides information on requirements regarding infrastructure and personnel as well as important medicolegal aspects of second opinion reports and interdisciplinary boards. It also reveals the striking discrepancy between the effort required by an institute and the inadequate reflection regarding remuneration in the billing systems.

Language network reorganization before and after temporal lobe epilepsy surgery

OBJECTIVE

Epilepsy surgery is the recommended treatment option for patients with drug-resistant temporal lobe epilepsy (TLE). This method offers a good chance of seizure freedom but carries a considerable risk of postoperative language impairment. The extremely variable neurocognitive profiles in surgical epilepsy patients cannot be fully explained by extent of resection, fiber integrity, or current task-based functional MRI (fMRI). In this study, the authors aimed to investigate pathology- and surgery-triggered language organization in TLE by using fMRI activation and network analysis as well as considering structural and neuropsychological measures.

METHODS

Twenty-eight patients with unilateral TLE (16 right, 12 left) underwent T1-weighted imaging, diffusion tensor imaging, and task-based language fMRI pre- and postoperatively (n = 15 anterior temporal lobectomy, n = 11 selective amygdalohippocampectomy, n = 2 focal resection). Twenty-two healthy subjects served as the control cohort. Functional connectivity, activation maps, and laterality indices for language dominance were analyzed from fMRI data. Postoperative fractional anisotropy values of 7 major tracts were calculated. Naming, semantic, and phonematic verbal fluency scores before and after surgery were correlated with imaging parameters.

RESULTS

fMRI network analysis revealed widespread, bihemispheric alterations in language architecture that were not captured by activation analysis. These network changes were found preoperatively and proceeded after surgery with characteristic patterns in the left and right TLEs. Ipsilesional fronto-temporal connectivity decreased in both left and right TLE. In left TLE specifically, preoperative atypical language dominance predicted better postoperative verbal fluency and naming function. In right TLE, left frontal language dominance correlated with good semantic verbal fluency before and after surgery, and left fronto-temporal language laterality predicted good naming outcome. Ongoing seizures after surgery (Engel classes ID-IV) were associated with naming deterioration irrespective of seizure side. Functional findings were not explained by the extent of resection or integrity of major white matter tracts.

CONCLUSIONS

Functional connectivity analysis contributes unique insight into bihemispheric remodeling processes of language networks after epilepsy surgery, with characteristic findings in left and right TLE. Presurgical contralateral language recruitment is associated with better postsurgical language outcome in left and right TLE.

Machine-learning identifies Parkinson's disease patients based on resting-state between-network functional connectivity

OBJECTIVE

Evaluation of a data-driven, model-based classification approach to discriminate idiopathic Parkinson's disease (PD) patients from healthy controls (HC) based on between-network connectivity in whole-brain resting-state functional MRI (rs-fMRI).

METHODS

Whole-brain rs-fMRI (EPI, TR = 2.2 s, TE = 30 ms, flip angle = 90°. resolution = 3.1 × 3.1 × 3.1 mm, acquisition time ≈ 11 min) was assessed in 42 PD patients (medical OFF) and 47 HC matched for age and gender. Between-network connectivity based on full and L2-regularized partial correlation measures were computed for each subject based on canonical functional network architectures of two cohorts at different levels of granularity (Human Connectome Project: 15/25/50/100/200 networks; 1000BRAINS: 15/25/50/70 networks). A Boosted Logistic Regression model was trained on the correlation matrices using a nested cross-validation (CV) with 10 outer and 10 inner folds for an unbiased performance estimate, treating the canonical functional network architecture and the type of correlation as hyperparameters. The number of boosting iterations was fixed at 100. The model with the highest mean accuracy over the inner folds was trained using an non-nested 10-fold 20-repeats CV over the whole dataset to determine feature importance.

RESULTS

Over the outer folds the mean accuracy was found to be 76.2% (median 77.8%, SD 18.2, IQR 69.4 - 87.1%). Mean sensitivity was 81% (median 80%, SD 21.1, IQR 75 - 100%) and mean specificity was 72.7% (median 75%, SD 20.4, IQR 66.7 - 80%). The 1000BRAINS 50-network-parcellation, using full correlations, performed best over the inner folds. The top features predominantly included sensorimotor as well as sensory networks.

CONCLUSION

A rs-fMRI whole-brain-connectivity, data-driven, model-based approach to discriminate PD patients from healthy controls shows a very good accuracy and a high sensitivity. Given the high sensitivity of the approach, it may be of use in a screening setting.

ADVANCES IN KNOWLEDGE

Resting-state functional MRI could prove to be a valuable, non-invasive neuroimaging biomarker for neurodegenerative diseases. The current model-based, data-driven approach on whole-brain between-network connectivity to discriminate Parkinson's disease patients from healthy controls shows promising results with a very good accuracy and a very high sensitivity.

Prediction of outcome after aneurysmal subarachnoid haemorrhage using data from patient admission

OBJECTIVES

The pathogenesis leading to poor functional outcome after aneurysmal subarachnoid haemorrhage (aSAH) is multifactorial and not fully understood. We evaluated a machine learning approach based on easily determinable clinical and CT perfusion (CTP) features in the course of patient admission to predict the functional outcome 6 months after ictus.

METHODS

Out of 630 consecutive subarachnoid haemorrhage patients (2008-2015), 147 (mean age 54.3, 66.7% women) were retrospectively included (Inclusion: aSAH, admission within 24 h of ictus, CTP within 24 h of admission, documented modified Rankin scale (mRS) grades after 6 months. Exclusion: occlusive therapy before first CTP, previous aSAH, CTP not evaluable). A random forests model with conditional inference trees was optimised and trained on sex, age, World Federation of Neurosurgical Societies (WFNS) and modified Fisher grades, aneurysm in anterior vs. posterior circulation, early external ventricular drainage (EVD), as well as MTT and T_max maximum, mean, standard deviation (SD), range, 75th quartile and interquartile range to predict dichotomised mRS (≤ 2; > 2). Performance was assessed using the balanced accuracy over the training and validation folds using 20 repeats of 10-fold cross-validation.

RESULTS

In the final model, using 200 trees and the synthetic minority oversampling technique, median balanced accuracy was 84.4% (SD 0.7) over the training folds and 70.9% (SD 1.2) over the validation folds. The five most important features were the modified Fisher grade, age, MTT range, WFNS and early EVD.

CONCLUSIONS

A random forests model trained on easily determinable features in the course of patient admission can predict the functional outcome 6 months after aSAH with considerable accuracy.

KEY POINTS

• Features determinable in the course of admission of a patient with aneurysmal subarachnoid haemorrhage (aSAH) can predict the functional outcome 6 months after the occurrence of aSAH. • The top five predictive features were the modified Fisher grade, age, the mean transit time (MTT) range from computed tomography perfusion (CTP), the WFNS grade and the early necessity for an external ventricular drainage (EVD). • The range between the minimum and the maximum MTT may prove to be a valuable biomarker for detrimental functional outcome.

[Epidemiology of echinococcosis in Bavaria]

OBJECTIVE

There are few and incomplete data about the epidemiology of echinococcosis in Germany. The aim of this retrospective study was to collect informations about frequency and distribution of this parasitosis in one of the main endemic regions (Bavaria).

PATIENTS AND METHODS

Standardized questionnaires were sent to all Bavarian hospitals, requesting (anonymous) information about all patients with echinococcosis seen between 1985 and 1989. In addition, hospital statistics and archives were searched for echinococcosis cases. A total of 216 cases were found; sufficient data were available for 181 (87 males, 94 females; mean age 41 [4-79] years). There were 123 patients with cystic echinococcosis (infection with the larval stage of Echinococcus granulosus), 58 with the alveolar form (larval stage of Echinococcus multilocularis). In the remaining 35 the available information was inadequate for reliable differentiation.

RESULTS

The data indicate a prevalence of echinococcosis in Bavaria of 1.9 per 100,000 inhabitants, 1.1 for Echinococcus granulosus and 0.5 for Echinococcus multilocularis. The mean annual incidence was 0.22 (Echinococcus granulosus 0.15; Echinococcus multilocularis 0.03). Dividing the patients by country of origin, 86.2% of those with Echinococcus multilocularis were German, while 68.3% of those with Echinococcus granulosus originated from outside Germany, mostly the Mediterranean area. The prevalence of Echinococcus multilocularis infection was highest in the District of Swabia (2.4/100,000) and Upper Bavaria (0.6/100,000). These are regions in which there is a proven significantly higher infestation of echinococcosis in foxes. Farmers were most at risk of being infected with alveolar echinococcosis.

Epidemiology of alveolar echinococcosis in southern Germany (Bavaria)

Alveolar echinococcosis is considered to be the most dangerous endemic parasitic disease for man in Central Europe. In Germany, unlike the neighbouring countries of Switzerland, Austria and France, only limited data on the prevalence and incidence of echinococcosis are available. Therefore, a retrospective cross-sectional study was conducted in order to investigate the epidemiology of echinococcosis in Bavaria, one of the two southern states of Germany. A standardised questionnaire was sent to all hospitals in Bavaria requesting information about patients seen from 1985 to 1989. In a second step a team of reviewers was sent to all relevant hospitals for active case finding in hospital statistics and medical records. A total of 216 patients with echinococcosis were detected of whom 58 had alveolar echinococcosis. According to these data, the prevalence in Bavaria was calculated to be 0.5 per 100,000 inhabitants with peak values in the counties of Swabia (2.4) and Upper Bavaria (0.6). The annual mean incidence of newly diagnosed cases amounted to 0.03 per 100,000. The distribution of prevalence in man was closely correlated to the infection rates in foxes throughout Bavaria (p < 0.05). Farmers are the occupational group with the highest risk to acquire echinococcosis with a prevalence/odds ratio of 14.6 for Swabia and 8.8 for Upper Bavaria, when compared to the general rural population.