Clinical application of the "sellar barrier's concept" for predicting intraoperative CSF leak in endoscopic endonasal surgery for pituitary adenomas with a machine learning analysis.

Background: Recently, it was defined that the sellar barrier entity could be identified as a predictor of cerebrospinal fluid (CSF) intraoperative leakage. The aim of this study is to validate the application of the sellar barrier concept for predicting intraoperative CSF leak in endoscopic endonasal surgery for pituitary adenomas with a machine learning approach. Methods: We conducted a prospective cohort study, from June 2019 to September 2020: data from 155 patients with pituitary subdiaphragmatic adenoma operated through endoscopic approach at the Division of Neurosurgery, Università degli Studi di Napoli "Federico II," were included. Preoperative magnetic resonance images (MRI) and intraoperative findings were analyzed. After processing patient data, the experiment was conducted as a novelty detection problem, splitting outliers (i.e., patients with intraoperative fistula, n = 11/155) and inliers into separate datasets, the latter further separated into training (n = 115/144) and inlier test (n = 29/144) datasets. The machine learning analysis was performed using different novelty detection algorithms [isolation forest, local outlier factor, one-class support vector machine (oSVM)], whose performance was assessed separately and as an ensemble on the inlier and outlier test sets. Results: According to the type of sellar barrier, patients were classified into two groups, i.e., strong and weak barrier; a third category of mixed barrier was defined when a case was neither weak nor strong. Significant differences between the three datasets were found for Knosp classification score (p = 0.0015), MRI barrier: strong (p = 1.405 × 10-6), MRI barrier: weak (p = 4.487 × 10-8), intraoperative barrier: strong (p = 2.788 × 10-7), and intraoperative barrier: weak (p = 2.191 × 10-10). We recorded 11 cases of intraoperative leakage that occurred in the majority of patients presenting a weak sellar barrier (p = 4.487 × 10-8) at preoperative MRI. Accuracy, sensitivity, and specificity for outlier detection were 0.70, 0.64, and 0.72 for IF; 0.85, 0.45, and 1.00 for LOF; 0.83, 0.64, and 0.90 for oSVM; and 0.83, 0.55, and 0.93 for the ensemble, respectively. Conclusions: There is a true correlation between the type of sellar barrier at MRI and its in vivo features as observed during endoscopic endonasal surgery. The novelty detection models highlighted differences between patients who developed an intraoperative CSF leak and those who did not.

A Combined Radiomics and Machine Learning Approach to Overcome the Clinicoradiologic Paradox in Multiple Sclerosis.

BACKGROUND AND PURPOSE: Conventional MR imaging explains only a fraction of the clinical outcome variance in multiple sclerosis. We aimed to evaluate machine learning models for disability prediction on the basis of radiomic, volumetric, and connectivity features derived from routine brain MR images. MATERIALS AND METHODS: In this retrospective cross-sectional study, 3T brain MR imaging studies of patients with multiple sclerosis, including 3D T1-weighted and T2-weighted FLAIR sequences, were selected from 2 institutions. T1-weighted images were processed to obtain volume, connectivity score (inferred from the T2 lesion location), and texture features for an atlas-based set of GM regions. The site 1 cohort was randomly split into training (n = 400) and test (n = 100) sets, while the site 2 cohort (n = 104) constituted the external test set. After feature selection of clinicodemographic and MR imaging-derived variables, different machine learning algorithms predicting disability as measured with the Expanded Disability Status Scale were trained and cross-validated on the training cohort and evaluated on the test sets. The effect of different algorithms on model performance was tested using the 1-way repeated-measures ANOVA. RESULTS: The selection procedure identified the 9 most informative variables, including age and secondary-progressive course and a subset of radiomic features extracted from the prefrontal cortex, subcortical GM, and cerebellum. The machine learning models predicted disability with high accuracy (r approaching 0.80) and excellent intra- and intersite generalizability (r ≥ 0.73). The machine learning algorithm had no relevant effect on the performance. CONCLUSIONS: The multidimensional analysis of brain MR images, including radiomic features and clinicodemographic data, is highly informative of the clinical status of patients with multiple sclerosis, representing a promising approach to bridge the gap between conventional imaging and disability.

Alemtuzumab significantly improves posterior fossa syndrome presented as a relapse of multiple sclerosis.

BACKGROUND: Posterior fossa syndrome (PFS) is a rare manifestation of ponto-mesencephalic lesions frequently reported in post-surgical pediatric tumors, rarely described as a consequence of vascular, infective or inflammatory lesions. OBJECTIVE: The aim of this article is to report the clinical and neuroradiological characteristics of a patient with an acute PFS presentation as a relapse in relapsing-remitting MS, significantly responsive to Alemtuzumab treatment. CASE REPORT: 24-year-old patient affected by multiple sclerosis developed motor-cognitive and behavioral syndrome related to an extensive ponto-mesencephalic lesion under Fingolimod treatment. CONCLUSION: Our case highlights the significant and rapid effect of Alemtuzumab therapy on both cognitive and motor symptoms occurring during a MS relapse with atypical neuroradiological localization.

Persisting Embryonal Infundibular Recess in Morning Glory Syndrome: Clinical Report of a Novel Association.

Morning glory syndrome is characterized by a congenital optic disc defect that resembles the eponymous flower. We present the MR imaging findings of 2 pediatric patients with morning glory disc anomaly and persisting embryonal infundibular recess, another rare malformative finding, a previously unreported association. Neuroradiologists should be aware of the possible presence of a persisting embryonal infundibular recess in patients with morning glory syndrome, to aid in the differential diagnosis including other pituitary malformations such as pituitary stalk duplication.

Diagnostic work-up in obstructive and inflammatory salivary gland disorders.

Inflammatory and obstructive disorders of the salivary glands are caused by very different pathological conditions affecting the gland tissue and/or the excretory system. The clinical setting is essential to address the appropriate diagnostic imaging work-up. According to history and physical examination, four main clinical scenarios can be recognised: (1) acute generalised swelling of major salivary glands; (2) acute swelling of a single major salivary gland; (3) chronic generalised swelling of major salivary glands, associated or not with "dry mouth"; (4) chronic or prolonged swelling of a single major salivary gland. The algorithm for imaging salivary glands depends on the scenario with which the patient presents to the clinician. Imaging is essential to confirm clinical diagnosis, define the extent of the disease and identify complications. Imaging techniques include ultrasound (US), computed tomography (CT) and magnetic resonance (MR) with MR sialography.

Imaging of post-surgical treatment and of related complications in spinal trauma.

Spinal trauma is a devastating event with a high morbidity and mortality. The rationale of imaging is to diagnose the traumatic abnormalities and characterize the type of injury, to estimate the severity of the lesions, to evaluate the potential spinal instability. In case of spinal instability, the goals of operative treatment are decompression of the spinal cord canal and stabilization of the disrupted vertebral column. Particularly, diagnostic imaging, mainly by CT and MR, has a main role in the post-treatment evaluation. The neuroradiological evaluation of the postoperative spine requires a general knowledge of the surgical approach to each spinal region and of the normal temporal evolution of expected postoperative changes. The neuroradiologist should evaluate the devices implanted, their related complications and promptly alert the surgeon of acute complications, mainly vascular and infective. During the follow-up, it is mandatory to know and search chronic complications as pseudomeningocele, accelerated degenerative disease, arachnoiditis, peridural fibrosis. Knowledge of specific complications relating to each surgical approach will assist the neuroradiologist in interpretation of postoperative images.