Real-world challenges in the diagnosis of primary progressive multiple sclerosis.

BACKGROUND AND PURPOSE: Despite the 2017 revisions to the McDonald criteria, diagnosing primary progressive multiple sclerosis (PPMS) remains challenging. To improve clinical practice, the aim was to identify frequent diagnostic challenges in a real-world setting and associate these with the performance of the 2010 and 2017 PPMS diagnostic McDonald criteria. METHODS: Clinical, radiological and laboratory characteristics at the time of diagnosis were retrospectively recorded from designated PPMS patient files. Possible complicating factors were recorded such as confounding comorbidity, signs indicative of alternative diagnoses, possible earlier relapses and/or incomplete diagnostic work-up (no cerebrospinal fluid examination and/or magnetic resonance imaging brain and spinal cord). The percentages of patients fulfilling the 2010 and 2017 McDonald criteria were calculated after censoring patients with these complicating factors. RESULTS: A total of 322 designated PPMS patients were included. Of all participants, it was found that n = 28/322 had confounding comorbidity and/or signs indicative of alternative diagnoses, n = 103/294 had possible initial relapsing and/or uncertainly progressive phenotypes and n = 73/191 received an incomplete diagnostic work-up. When applying the 2010 and 2017 diagnostic PPMS McDonald criteria on n = 118 cases with a full diagnostic work-up and a primary progressive disease course without a better alternative explanation, these were met by 104/118 (88.1%) and 98/118 remaining patients (83.1%), respectively (p = 0.15). CONCLUSION: Accurate interpretation of the initial clinical course, consideration of alternative diagnoses and a full diagnostic work-up are the cornerstones of a PPMS diagnosis. When these conditions are met, the 2010 and 2017 McDonald criteria for PPMS perform similarly, emphasizing the importance of their appropriate application in clinical practice.

MRI criteria differentiating asymptomatic PML from new MS lesions during natalizumab pharmacovigilance.

OBJECTIVE: Differentiation between progressive multifocal leukoencephalopathy (PML) and new multiple sclerosis (MS) lesions on brain MRI during natalizumab pharmacovigilance in the absence of clinical signs and symptoms is challenging but is of substantial clinical relevance. We aim to define MRI characteristics that can aid in this differentiation. METHODS: Reference and follow-up brain MRIs of natalizumab-treated patients with MS with asymptomatic PML (n=21), or asymptomatic new MS lesions (n=20) were evaluated with respect to characteristics of newly detected lesions by four blinded raters. We tested the association with PML for each characteristic and constructed a multivariable prediction model which we analysed using a receiver operating characteristic (ROC) curve. RESULTS: Presence of punctate T2 lesions, cortical grey matter involvement, juxtacortical white matter involvement, ill-defined and mixed lesion borders towards both grey and white matter, lesion size of >3 cm, and contrast enhancement were all associated with PML. Focal lesion appearance and periventricular localisation were associated with new MS lesions. In the multivariable model, punctate T2 lesions and cortical grey matter involvement predict for PML, while focal lesion appearance and periventricular localisation predict for new MS lesions (area under the curve: 0.988, 95% CI 0.977 to 1.0, sensitivity: 100%, specificity: 80.6%). INTERPRETATION: The MRI characteristics of asymptomatic natalizumab-associated PML lesions proved to differ from new MS lesions. This led to a prediction model with a high discriminating power. Careful assessment of the presence of punctate T2 lesions, cortical grey matter involvement, focal lesion appearance and periventricular localisation allows for an early diagnosis of PML.

Diagnostic performance of brain MRI in pharmacovigilance of natalizumab-treated MS patients.

BACKGROUND: In natalizumab-treated multiple sclerosis (MS) patients, magnetic resonance imaging (MRI) is considered as a sensitive tool in detecting both MS disease activity and progressive multifocal leukoencephalopathy (PML). OBJECTIVE: To investigate the performance of neuroradiologists using brain MRI in detecting new MS lesions and asymptomatic PML lesions and in differentiating between MS and PML lesions in natalizumab-treated MS patients. The secondary aim was to investigate interrater variability. METHODS: In this retrospective diagnostic study, four blinded neuroradiologists assessed reference and follow-up brain MRI scans of 48 natalizumab-treated MS patients with new asymptomatic PML lesions (n = 21) or new MS lesions (n = 20) or no new lesions (n = 7). Sensitivity and specificity for detection of new lesions in general (MS and PML lesions), MS and PML lesion differentiation, and PML detection were determined. Interrater agreement was calculated. RESULTS: Overall sensitivity and specificity for the detection of new lesions, regardless of the nature of the lesions, were 77.4% and 89.3%, respectively; for PML-MS lesion differentiation, 74.2% and 84.7%, respectively; and for asymptomatic PML lesion detection, 59.5% and 91.7%, respectively. Interrater agreement for the tested categories was fair to moderate. CONCLUSION: The diagnostic performance of trained neuroradiologists using brain MRI in pharmacovigilance of natalizumab-treated MS patients is moderately good. Interrater agreement among trained readers is fair to moderate.

Effect of CT reconstruction settings on the performance of a deep learning based lung nodule CAD system.

PURPOSE: To study the effect of different reconstruction parameter settings on the performance of a commercially available deep learning based pulmonary nodule CAD system. MATERIALS AND METHODS: We performed a retrospective analysis of 24 chest CT scans, reconstructed at 16 different reconstruction settings for two different iterative reconstruction algorithms (SAFIRE and ADMIRE) varying in slice thickness, kernel size and iterative reconstruction level strength using a commercially available deep learning pulmonary nodule CAD system. The DL-CAD software was evaluated at 25 different sensitivity threshold settings and nodules detected by the DL-CAD software were matched against a reference standard based on the consensus reading of three radiologists. RESULTS: A total of 384 CT reconstructions was analysed from 24 patients, resulting in a total of 5786 found nodules. We matched the detected nodules against the reference standard, defined by a team of thoracic radiologists, and showed a gradual drop in recall, and an improvement in precision when the iterative strength levels were increased for a constant kernel size. The optimal DL-CAD threshold setting for use in our clinical workflow was found to be 0.88 with an F2 of 0.73 ±â€¯0.053. CONCLUSIONS: The DL-CAD system behaves differently on IR data than on FBP data, there is a gradual drop in recall, and growth in precision when the iterative strength levels are increased. As a result, caution should be taken when implementing deep learning software in a hospital with multiple CT scanners and different reconstruction protocols. To the best of our knowledge, this is the first study that demonstrates this result from a DL-CAD system on clinical data.

[A man with severe traumatic brain injury]. / Een man met ernstig traumatisch schedelhersenletsel.

We present a 41-year-old man with severe traumatic brain injury. Cranial imaging studies revealed cerebral contusion and a longitudinal fracture of the temporal bone. Several days later brain herniated into the left external auditory canal. Imaging studies showed the known skull fracture with a direct connection between the external acoustic meatus and the intracranial structures.