[How do I investigate... the vulnerability of a carotid artery plaque in 2024]. / Comment j'explore la vulnérablilité d'une plaque carotidienne en 2024.

Carotid artery atherosclerosis is one of the leading causes of stroke. Even though the association between the risk of stroke and the level of morphological stenosis of a carotid plaque has been known for a long time, growing evidence has since proven necessary to assess the composition of the plaque itself to identify vulnerability predictors. These vulnerable plaques, even more if non-stenosing, may be responsible for a significant - but hard to quantify - proportion of strokes so far classified cryptogenic. As a matter of fact, plaque composition may escape detection and characterisation with classical imaging. Several biomarkers associated with its vulnerability to destabilization and with the risk of stroke such as intraplaque hemorrhage and inflammation are now routinely assessable. After a few pathophysiological reminders and a critical reading of the historical literature concerning carotid artery atherosclerosis management, we will review in this article the imaging techniques that can be used in the routine work-up of a carotid atherosclerotic plaque, with a focus on vessel wall magnetic resonance imaging and contrast enhanced ultrasonography.

L'athérosclérose carotidienne est une des causes les plus fréquentes d'accident ischémique cérébral (AIC). Si la dangerosité d'une plaque d'athérome est historiquement vue uniquement à travers le prisme de la sténose qu'elle engendre, l'évolution des connaissances nous pousse à considérer sa composition à la recherche de facteurs de vulnérabilité. Ces plaques à risque, a fortiori «non sténosantes¼, sont responsables d'une proportion difficilement quantifiable, mais probablement non négligeable d'AIC jusqu'ici considérés cryptogéniques. En effet, ces critères échappent pour beaucoup aux méthodes d'imagerie traditionnelle. Plusieurs propriétés associées à la vulnérabilité de la plaque et au risque d'AIC, principalement l'hémorragie intra-plaque et l'inflammation, sont désormais accessibles en pratique courante. Après quelques rappels physiopathologiques et une lecture critique de la littérature historique de la prise en charge de l'athérome carotidien, nous passerons en revue les différentes techniques d'imagerie utilisables en routine dans la mise au point de la plaque d'athérosclérose, avec un focus pratique sur l'imagerie pariétale vasculaire par résonance magnétique et, dans une moindre mesure, par échographie de contraste.

[Multifocal cerebral venous thrombosis]. / Cas clinique. Thrombose veineuse cérébrale multifocale.

Cerebral venous thrombosis is a rare condition and represents a neurological emergency. It is a particular subtype of stroke, characterized by a huge diversity of neurological symptoms. Due to the diversity of its potential clinical presentations, medical imaging plays an important role in its early detection, even on non-dedicated examinations often performed in search of another acute neurological pathology. The aim of this case report is to illustrate the different radiological signs of cerebral venous thrombosis and to discuss the difficulties in diagnosing it by imaging at the acute stage.

La thrombose veineuse cérébrale (TVC) est une pathologie rare et constitue une urgence neurologique. Il s'agit d'un sous-type d'accident vasculaire cérébral (AVC) particulier, aux manifestations symptomatiques neurologiques très variées. De par la diversité de ses potentielles présentations cliniques, l'imagerie médicale joue un rôle important dans sa détection précoce et ce, y compris sur des examens non dédiés, réalisés à la recherche d'une autre pathologie neurologique aiguë. L'objectif de ce cas clinique est d'illustrer les différents signes radiologiques de la TVC et d'insister sur les difficultés, au stade aigu, de poser son diagnostic par imagerie.

A review in radiomics: Making personalized medicine a reality via routine imaging.

Radiomics is the quantitative analysis of standard-of-care medical imaging; the information obtained can be applied within clinical decision support systems to create diagnostic, prognostic, and/or predictive models. Radiomics analysis can be performed by extracting hand-crafted radiomics features or via deep learning algorithms. Radiomics has evolved tremendously in the last decade, becoming a bridge between imaging and precision medicine. Radiomics exploits sophisticated image analysis tools coupled with statistical elaboration to extract the wealth of information hidden inside medical images, such as computed tomography (CT), magnetic resonance (MR), and/or Positron emission tomography (PET) scans, routinely performed in the everyday clinical practice. Many efforts have been devoted in recent years to the standardization and validation of radiomics approaches, to demonstrate their usefulness and robustness beyond any reasonable doubts. However, the booming of publications and commercial applications of radiomics approaches warrant caution and proper understanding of all the factors involved to avoid "scientific pollution" and overly enthusiastic claims by researchers and clinicians alike. For these reasons the present review aims to be a guidebook of sorts, describing the process of radiomics, its pitfalls, challenges, and opportunities, along with its ability to improve clinical decision-making, from oncology and respiratory medicine to pharmacological and genotyping studies.

A Rare Case of Extrinsic Stenosis of Dural Sinus.

Teaching Point: Dural sinus stenosis are a rare cause of increased intracranial pressure and can be treated in some cases by stenting.

An externally validated fully automated deep learning algorithm to classify COVID-19 and other pneumonias on chest computed tomography.

Purpose: In this study, we propose an artificial intelligence (AI) framework based on three-dimensional convolutional neural networks to classify computed tomography (CT) scans of patients with coronavirus disease 2019 (COVID-19), influenza/community-acquired pneumonia (CAP), and no infection, after automatic segmentation of the lungs and lung abnormalities. Methods: The AI classification model is based on inflated three-dimensional Inception architecture and was trained and validated on retrospective data of CT images of 667 adult patients (no infection n=188, COVID-19 n=230, influenza/CAP n=249) and 210 adult patients (no infection n=70, COVID-19 n=70, influenza/CAP n=70), respectively. The model's performance was independently evaluated on an internal test set of 273 adult patients (no infection n=55, COVID-19 n= 94, influenza/CAP n=124) and an external validation set from a different centre (305 adult patients: COVID-19 n=169, no infection n=76, influenza/CAP n=60). Results: The model showed excellent performance in the external validation set with area under the curve of 0.90, 0.92 and 0.92 for COVID-19, influenza/CAP and no infection, respectively. The selection of the input slices based on automatic segmentation of the abnormalities in the lung reduces analysis time (56 s per scan) and computational burden of the model. The Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis (TRIPOD) score of the proposed model is 47% (15 out of 32 TRIPOD items). Conclusion: This AI solution provides rapid and accurate diagnosis in patients suspected of COVID-19 infection and influenza.

Added Value of Dual-Energy CT in COVID-19 Pneumopathy.

Teaching point: The use of dual-energy instead of conventional single-energy computed tomography pulmonary angiogram can provide additional value concerning the diagnosis of COVID-19 and its complications, especially in the detection of small pulmonary embolism.

Oily Ascites Revealing a Spontaneous Rupture of a Mature Ovarian Cystic Teratoma.

Teaching point: Ovarian teratoma rupture can manifest clinically as an acute or chronic syndrome, associated with specific imaging features, both characterized by intra-abdominal fatty fluid.

A case of aphasia due to temporobasal edema: Contemporary models of language anatomy are clinically relevant.

BACKGROUND: Understanding the anatomy of language in the human brain is crucial for neurosurgical decision making and complication avoidance. The traditional anatomical models of human language, relying on relatively simple and rigid concepts of brain connectivity, cannot explain all clinical observations. The clinical case reported here illustrates the relevance of more recent concepts of language networks involving white matter tracts and their connections. CASE DESCRIPTION: Postoperative edema of the ventral occipitotemporal cortex, where modern network models locate a crucial language hub, resulted in transient severe aphasia after a subtemporal approach. Both verbal comprehension and expression were lost. The resolution of edema was associated with complete recovery from phonetic and semantic dysfunction. CONCLUSION: Complete aphasia due to a functional disturbance remote from the areas of Broca and Wernicke could be explained by contemporary neuroanatomical concepts of white matter connectivity. Knowledge of network-based models is relevant in brain surgery complication avoidance.

Development and Validation of an Automated Radiomic CT Signature for Detecting COVID-19.

The coronavirus disease 2019 (COVID-19) outbreak has reached pandemic status. Drastic measures of social distancing are enforced in society and healthcare systems are being pushed to and beyond their limits. To help in the fight against this threat on human health, a fully automated AI framework was developed to extract radiomics features from volumetric chest computed tomography (CT) exams. The detection model was developed on a dataset of 1381 patients (181 COVID-19 patients plus 1200 non COVID control patients). A second, independent dataset of 197 RT-PCR confirmed COVID-19 patients and 500 control patients was used to assess the performance of the model. Diagnostic performance was assessed by the area under the receiver operating characteristic curve (AUC). The model had an AUC of 0.882 (95% CI: 0.851-0.913) in the independent test dataset (641 patients). The optimal decision threshold, considering the cost of false negatives twice as high as the cost of false positives, resulted in an accuracy of 85.18%, a sensitivity of 69.52%, a specificity of 91.63%, a negative predictive value (NPV) of 94.46% and a positive predictive value (PPV) of 59.44%. Benchmarked against RT-PCR confirmed cases of COVID-19, our AI framework can accurately differentiate COVID-19 from routine clinical conditions in a fully automated fashion. Thus, providing rapid accurate diagnosis in patients suspected of COVID-19 infection, facilitating the timely implementation of isolation procedures and early intervention.