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OBJECTIVES: Evaluate the performance of a deep learning (DL)-based model for multiple sclerosis (MS) lesion segmentation and compare it to other DL and non-DL algorithms. METHODS: This ambispective, multicenter study assessed the performance of a DL-based model for MS lesion segmentation and compared it to alternative DL- and non-DL-based methods. Models were tested on internal (n = 20) and external (n = 18) datasets from Latin America, and on an external dataset from Europe (n = 49). We also examined robustness by rescanning six patients (n = 6) from our MS clinical cohort. Moreover, we studied inter-human annotator agreement and discussed our findings in light of these results. Performance and robustness were assessed using intraclass correlation coefficient (ICC), Dice coefficient (DC), and coefficient of variation (CV). RESULTS: Inter-human ICC ranged from 0.89 to 0.95, while spatial agreement among annotators showed a median DC of 0.63. Using expert manual segmentations as ground truth, our DL model achieved a median DC of 0.73 on the internal, 0.66 on the external, and 0.70 on the challenge datasets. The performance of our DL model exceeded that of the alternative algorithms on all datasets. In the robustness experiment, our DL model also achieved higher DC (ranging from 0.82 to 0.90) and lower CV (ranging from 0.7 to 7.9%) when compared to the alternative methods. CONCLUSION: Our DL-based model outperformed alternative methods for brain MS lesion segmentation. The model also proved to generalize well on unseen data and has a robust performance and low processing times both on real-world and challenge-based data. CLINICAL RELEVANCE STATEMENT: Our DL-based model demonstrated superior performance in accurately segmenting brain MS lesions compared to alternative methods, indicating its potential for clinical application with improved accuracy, robustness, and efficiency. KEY POINTS: • Automated lesion load quantification in MS patients is valuable; however, more accurate methods are still necessary. • A novel deep learning model outperformed alternative MS lesion segmentation methods on multisite datasets. • Deep learning models are particularly suitable for MS lesion segmentation in clinical scenarios.

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INTRODUCTION: Neuralgic amyotrophy (NA) is a painful non-traumatic peripheral nervous system condition affecting the brachial plexus. Signal abnormalities in nerves and muscles have been detected in these patients using magnetic resonance neurography (MRN). METHODS: Electronic medical records and MRN images obtained in a 3 T scanner, in 14 adult patients diagnosed with NA at our Neurological institution (Neuromuscular Disorders Section), between December 2015 and December 2019 were retrospectively reviewed. The study was first approved by our Institutional Ethics Committee. RESULTS: Subclinical, multifocal, and bilateral nerve signal anomalies were recorded in the brachial plexus of these patients. We identified four different types of nerve constriction without entrapment, which we categorized as follows: incomplete focal (type I), complete focal or hourglass (type II), multifocal or string of pearls (type III) and segmental (type IV). CONCLUSIONS: Given that MRN is an accurate diagnostic tool to detect nerve damage, we believe abnormal findings could improve early detection of NA patients.

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El origen subcortical de la epilepsia ha sido tema de profundo debate durante muchísimo tiempo y solo se le otorgaba a las estructural subcorticales un rol en la distribución, modulación y alteración de la actividad cortical. Sin embargo, los estudios neurofisiológicos y de neuroimágenes de las últimas décadas han provisto de la información suficiente como para confirmar que algunas crisis epilépticas pueden iniciarse en estructuras subcorticales. Presentamos 4 pacientes pediátricos con lesiones cerebelosas y epilepsia refractaria, revisamos la bibliografía, analizamos las formas de presentación, los hallazgos neurofisiológicos y resultados a largo plazo con cirugía

The subcortical origin of epilepsy has been a subject of debate and only the subcortical structures were given a role in the distribution, modulation and alteration of cortical activity. However, neurophysiological and neuroimaging studies of recent decades have provided enough information to confirm the onset of some epileptic seizures in subcortical structures. We present 4 pediatric patients with cerebellar lesions and refractory epilepsy, we reviewed the literature, analyzed the forms of presentation, the neurophysiological findings and long-term results with surgery

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BACKGROUND AND PURPOSE: Multinodular and Vacuolating Neuronal Tumor of the cerebrum (MVNT) is a benign -seizure associated- lesion affecting mostly adults. This new entity has been included in the 2016 World Health Organization classification of tumors of the central nervous system. Its pathologic hallmark consist of a subcortical cluster of nodular lesions located on the subcortical white matter. We aim to report a series of cases of presumed MVNT observed in our institution and review the literature. MATERIALS AND METHODS: In this retrospective study, a search was performed on our hospital information system. Sixteen cases were included. Demographic, clinical and radiological features were detailed in a table. All patients had an MRI acquired either on a 1.5 or a 3 Tesla scanner. Sequences performed included T1, T2, GRE/SWI, T2 FLAIR and DWI. Gadolinium enhanced T1-WI wer available in 11 patients and follow-up MRI were available in 7 patients. RESULTS: Patient ages ranged from 16 to 77 years (mean 42 years). Seizure and non-focal headache were by far the most common neurological complaints for which MRI was requested. All lesions consisted of clusters of multiple, discrete, round or ovoid, intra-axial, FLAIR and T2-WI hyperintense nodules. Follow-up MRI scans showed no changes between studies. CONCLUSIONS: MVNT is a benign, stable lesion that exhibits a typical radiological pattern that most of the times sufficed to arrive to a diagnosis, without the need of pathological confirmation. We confirm that our demographic, clinical and radiological findings are in accordance with those published in international literature.

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The pudendal nerve entrapment is an entity understudied by diagnosis imaging. Various causes are recognized in relation to difficult labors, rectal, perineal, urological and gynecological surgery, pelvic trauma fracture, bones tumors and compression by tumors or pelvic pseudotumors. Pudendal neuropathy should be clinically suspected, and confirmed by different methods such as electrofisiological testing: evoked potentials, terminal motor latency test and electromyogram, neuronal block and magnetic resonance imaging. The radiologist should be acquainted with the complex anatomy of the pelvic floor, particularly on the path of pudendal nerve studied by magnetic resonance imaging. High resolution magnetic resonance neurography should be used as a complementary diagnostic study along with clinical and electrophysiological examinations in patients with suspected pudendal nerve neuralgia.

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La neuralgia del nervio pudendo (NP) es una entidad poco estudiada por imágenes. Se reconocen varias causas, tales como compresión a través de su paso por estructuras ligamentarias; estiramiento por partos laboriosos; lesiones secundarias a cirugías rectales, perineales, urológicas y ginecológicas, traumatismos con o sin fractura de huesos pelvianos; procesos inflamatorios/autoinmunes; tumores del NP, y, compresión/desplazamiento por tumores o seudotumores de pelvis. El diagnóstico de neuralgia del NP se sospecha por la clínica y se confirma por diferentes métodos, tales como las pruebas electrofisiolológicas: potenciales evocados, test de latencia motora terminal y electromiograma, y, a través de bloqueos neurales y resonancia magnética. La neurografía por resonancia magnética de alta resolución, debería ser empleada como estudio diagnóstico complementario junto a la clínica y exámenes electrofisiológicos, en los pacientes con sospecha de neuralgia del NP.

The pudendal nerve entrapment is an entity understudied by diagnosis imaging. Various causes are recognized in relation to difficult labors, rectal, perineal, urological and gynecological surgery, pelvic trauma fracture, bones tumors and compression by tumors or pelvic pseudotumors. Pudendal neuropathy should be clinically suspected, and confirmed by different methods such as electrofisiological testing: evoked potentials, terminal motor latency test and electromyogram, neuronal block and magnetic resonance imaging. The radiologist should be acquainted with the complex anatomy of the pelvic floor, particularly on the path of pudendal nerve studied by magnetic resonance imaging. High resolution magnetic resonance neurography should be used as a complementary diagnostic study along with clinical and electrophysiological examinations in patients with suspected pudendal nerve neuralgia.

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