Corpus callosum in children with neurodevelopmental delay: MRI standard qualitative assessment versus automatic quantitative analysis

BACKGROUND

The corpus callosum (CC) is a key brain structure. In children with neurodevelopmental delay, we compared standard qualitative radiological assessments with an automatic quantitative tool.

METHODS

We prospectively enrolled 73 children (46 males, 63.0%) with neurodevelopmental delay at single university hospital between September 2020 and September 2022. All of them underwent 1.5-T brain magnetic resonance imaging (MRI) including a magnetization-prepared 2 rapid acquisition gradient echoes - MP2RAGE sequence. Two radiologists blindly reviewed the images to classify qualitatively the CC into normal, hypoplasic, hyperplasic, and/or dysgenetic classes. An automatic tool (QuantiFIRE) was used to provide brain volumetry and T1 relaxometry automatically as well as deviations of those parameters compared with a healthy age-matched cohort. The MRI reference standard for CC volumetry was based on the Garel et al. study. Cohen κ statistics was used for interrater agreement. The radiologists and QuantiFIRE's diagnostic accuracy were compared with the reference standard using the Delong test.

RESULTS

The CC was normal in 42 cases (57.5%), hypoplastic in 20 cases (27.4%), and hypertrophic in 11 cases (15.1%). T1 relaxometry values were abnormal in 26 children (35.6%); either abnormally high (18 cases, 24.6%) or low (8 cases, 11.0%). The interrater Cohen κ coefficient was 0.91. The diagnostic accuracy of the QuantiFIRE prototype was higher than that of the radiologists for hypoplastic and normal CC (p = 0.003 for both subgroups, Delong test).

CONCLUSIONS

An automated volumetric and relaxometric assessment can assist the evaluation of brain structure such as the CC, particularly in the case of subtle abnormalities.

RELEVANCE STATEMENT

Automated brain MRI segmentation combined with statistical comparison to normal volume and T1 relaxometry values can be a useful diagnostic support tool for radiologists.

KEY POINTS

• Corpus callosum abnormality detection is challenging but clinically relevant. • Automated quantitative volumetric analysis had a higher diagnostic accuracy than that of visual appreciation of radiologists. • Quantitative T1 relaxometric analysis might help characterizing corpus callosum better.

Chest computed tomography signs associated with pejorative evolution in COVID-19 patients

PURPOSE

The aim of this study was to evaluate how chest computed tomography (CT) can predict pejorative evolution in COVID-19 patients.

MATERIAL AND METHODS

Data on 349 consecutive patients who underwent a chest CT either for severe suspected COVID-19 pneumonia or clinical aggravation and with COVID-19 were retrospectively analysed. In total, 109 had laboratory-confirmed COVID-19 infection by a positive reverse-transcription polymerase chain reaction (RT-PCR) and were included. The main outcomes for pejorative evolution were death and the need for invasive endotracheal ventilation (IEV). All the CT images were retrospectively reviewed, to analyse the CT signs and semiologic patterns of pulmonary involvement.

RESULTS

Among the 109 COVID-19 patients, 73 (67%) had severe symptoms of COVID-19, 28 (25.7%) needed an IEV, and 11 (10.1%) died. The following signs were significantly associated with both mortality and need for IEV: traction bronchiectasis and total affected lung volume ≥ 50% (p < 10-3). Other CT signs were only associated with the need of IEV: vascular dilatation, air bubble sign, peribronchovascular thickening, interlobular thickening, and number of involved lobes ≥ 4 (p < 10-3).

CONCLUSIONS

On a chest CT performed during the first week of the symptoms, the presence of traction bronchiectasis and high values of affected lung volume are associated with the need for IEV, and with mortality, in COVID-19 patients.