Primary central nervous system lymphoma MRI

Primary central nervous system lymphoma MRI

Reported signal characteristics include:


Typically hypointense to grey matter

T1 C+ (Gd)

typical high-grade tumours show intense homogeneous enhancement while low-grade tumours have absent to moderate enhancement

Peripheral ring enhancement may be seen in immunocompromised patients (HIV/AIDS)



Majority are iso to hypointense to grey matter

Isointense: 33%

Hypointense: 20% 9 – when present this is a helpful distinguishing feature

Hyperintense: 15-47%, more common in tumours with necrosis


Restricted diffusion with ADC values lower than normal brain, typically between 400 and 600 x 10-6 mm2/s (lower than high-grade gliomas and metastases)

A number of studies have suggested that the lower the ADC values of the tumour the poorer the response to tumour and higher likelihood of recurrence

AADC is particularly useful in assessing response to chemotherapy, with increases in ADC values to above those of normal brain predictive of complete response

MR spectroscopy

Large choline peak

Reversed choline/creatinine ratio

Markedly decreased NAA

Lactate peak may also be seen

MR perfusion

Only modest if any increase in rCBV (much less marked than in high-grade gliomas, where angiogenesis is a prominent feature).


Precise volumetric assessment of brain tumors is relevant for treatment planning and monitoring. However, manual segmentations are time-consuming and impeded by intra- and inter rater variabilities.

To investigate the performance of a deep learning model (DLM) to automatically detect and segment primary central nervous system lymphoma (PCNSL) on clinical MRI.

Study type: Retrospective.

Population: Sixty-nine scans (at initial and/or follow-up imaging) from 43 patients with PCNSL referred for clinical MRI tumor assessment.

Field strength/sequence: T1 weighted image -/T2 weighted image, T1 -weighted contrast-enhanced (T1 CE), and FLAIR at 1.0, 1.5, and 3.0T from different vendors and study centers.

Fully automated voxelwise segmentation of tumor components was performed using a 3D convolutional neural network (DeepMedic) trained on gliomas (n = 220). DLM segmentations were compared to manual segmentations performed in a 3D voxelwise manner by two readers (radiologist and neurosurgeon; consensus reading) from T1 CE and FLAIR, which served as the reference standard.

Statistical tests: Dice similarity coefficient (DSC) for comparison of spatial overlap with the reference standard, Pearson’s correlation coefficient ® to assess the relationship between volumetric measurements of segmentations, and Wilcoxon rank-sum test for comparison of DSCs obtained in initial and follow-up imaging.

The DLM detected 66 of 69 PCNSL, representing a sensitivity of 95.7%. Compared to the reference standard, DLM achieved good spatial overlap for total tumor volume (TTV, union of tumor volume in T1 CE and FLAIR; average size 77.16 ± 62.4 cm3 , median DSC: 0.76) and tumor core (contrast enhancing tumor in T1 CE; average size: 11.67 ± 13.88 cm3 , median DSC: 0.73). High volumetric correlation between automated and manual segmentations was observed (TTV: r = 0.88, P < 0.0001; core: r = 0.86, P < 0.0001). Performance of automated segmentations was comparable between pretreatment and follow-up scans without significant differences (TTV: P = 0.242, core: P = 0.177).

Data conclusion: In clinical MRI scans, a DLM initially trained on gliomas provides segmentation of PCNSL comparable to manual segmentation, despite its complex and multifaceted appearance. Segmentation performance was high in both initial and follow-up scans, suggesting its potential for application in longitudinal tumor imaging.

Level of evidence: 3 TECHNICAL EFFICACY STAGE: 2 1).


Pennig L, Hoyer UCI, Goertz L, et al. Primary Central Nervous System Lymphoma: Clinical Evaluation of Automated Segmentation on Multiparametric MRI Using Deep Learning [published online ahead of print, 2020 Jul 13]. J Magn Reson Imaging. 2020;e27288. doi:10.1002/jmri.27288

Fungal Infections of the Central Nervous System Pathogens, Diagnosis, and Management

Fungal Infections of the Central Nervous System Pathogens, Diagnosis, and Management

by Mehmet Turgut (Editor), Sundaram Challa (Editor), Ali Akhaddar (Editor)

List Price:$199.99


This book provides comprehensive information on fungal infections of the central nervous system (CNS). Fungal infections are still a major public health challenge for most of the developing world and even for developed countries due to the rising numbers of immune compromised patients, refugee movements, and international travel. Although fungal infections involving the CNS are not particularly common, when they do occur, the results can be devastating in spite of recent advances and currently available therapies. Further, over the past several years, the incidence of these infections has seen a steep rise among immunodeficient patients. In this context, aggressive surgery remains the mainstay of management, but conservative antifungal drug treatment complemented by aggressive surgical debridement may be necessary. Yet the optimal management approach to fungal infections of the CNS remains controversial, owing to the limited individual experience and the variable clinical course of the conditions. Addressing that problem, this comprehensive book offers the ideal resource for neurosurgeons, neurologists and other specialists working with infectious diseases.

Central sulcus

Central sulcus

see also Central sulcus region

The central sulcus is a fold in the cerebral cortex in the brains of vertebrates. Also called the central fissure, it was originally called the fissure of Rolando or the Rolandic fissure, after Luigi Rolando. It is sometimes confused with the medial longitudinal fissure.

The central sulcus is a prominent landmark of the brain, separating the parietal lobe from the frontal lobe and the primary motor cortex from the primary somatosensory cortex.

The central sulcus joins the Sylvian fissure in only 2 % of cases.

During neurosurgical procedures, it is sometimes difficult to understand the cortical anatomy of this region.

In 68/82 hemispheres, the central sulcus did not reach the posterior ramus of the lateral sulcus. A knob on the second curve of the precentral gyrus was reliably identified in only 64/82 hemispheres 1).

Computed tomography scans of 71 adult patients with no pathological imaging were analyzed. The position of the bregma and the central sulcus was determined. The distances from bregma to the pre-central and post-central sulci were calculated. The relationships from the nasion and glabella to cortical structures were also assessed.

The mean distances between the bregma and the pre-central, central and post-central sulci were 26.8 ± 7.2; 47.8 ± 5.9 and 60.6 ± 5.7 mm, respectively, without gender discrepancy. The mean distance nasion-bregma and the nasion-related measures showed significant differences among sexes.

The central sulcus was located accurately, on average 47.8 mm behind the bregma, which should be used instead of nasion in order to avoid gender discrepancy. The data obtained provide useful and reliable information to guide neurosurgical procedures 2).

Central sulcus on axial imaging

Identification of the central sulcus is important to localize the motor strip (contained in the precentral gyrus). The central sulcus (CS) is visible on 93% of CTs and 100% of MRIs 3).

It curves posteriorly as it approaches the interhemispheric fissure (IHF), and often terminates in the paracentral lobule, just anterior to the pars marginalis (pM) within the pars bracket 4) (i.e. the CS often does not reach the midline).

Inferolateral portion

The inferolateral portion is difficult to identify if unable to trace the sulcus superoinferiorly. Su et al. observed that the cortex abutting the central sulcus appears isointense to the adjacent white matter on DWI, they named this the ‘invisible cortex sign’ and a study evaluated whether it could be used to identify the inferolateral central sulcus.

This observational study of 108 consecutive ‘normal’ MRI studies was performed from May 2016 to January 2017. A single axial DWI image – obtained in the anterior commissureposterior commissure plane – was selected from each scan just above the subcentral gyrus such that it included the most inferolateral portion of the central sulcus. These single images were given to 10 readers (neuroradiologists, a neuroradiology fellow and radiology trainees) who marked the central sulcus based on the presence of the ‘invisible cortex sign’. Their accuracy in identifying the central sulcus was compared with that of the principal investigators, who used tri-planar T1 volumetric MRI sequences.

One hundred and eight consecutive patients (55 female, 53 male) were selected, ranging from 18 to 81 years old (mean = 40.5, σ = 18.2). The central sulcus was correctly identified in 95.5% of cases (σ = 3.7%; range 89.4-99.1%).

The ‘invisible cortex sign’ is a highly accurate method of identifying the inferolateral central sulcus on a single axial DWI slice without relying on the more superior aspects of the sulcus 5)


Focal cortical dysplasias (FCDs) are mainly located in the frontal region, with a particular tropism for the central sulcus.



Rodrigues T, Rodrigues M, Paz D, Costa MD, Santos B, Braga V, Paiva Neto Md, Centeno R, Cavalheiro S, Chaddad-Neto F. Is the omega sign a reliable landmark for the neurosurgical team? An anatomical study about the central sulcus region. Arq Neuropsiquiatr. 2015 Nov;73(11):934-8. doi: 10.1590/0004-282×20150160. PubMed PMID: 26517217.

Oberman DZ, Rasmussen J, Toscano M, Goldschmidt E, Ajler P. Computed Tomographic Localization of the Central Sulcus: A Morphometric Study in Adult Patients. Turk Neurosurg. 2018;28(6):877-881. doi: 10.5137/1019-5149.JTN.21145-17.1. PubMed PMID: 29165746.
3) , 4)

Naidich TP, Brightbill TC. The pars marginalis, I: A “bracket” sign for the central sulcus in axial plane CT and MRI. Int J Neuroradiol. 1996; 2:3–19

Su S, Yang N, Gaillard F. Invisible cortex sign: A highly accurate feature to localize the inferolateral central sulcus. J Med Imaging Radiat Oncol. 2019 Aug;63(4):439-445. doi: 10.1111/1754-9485.12875. Epub 2019 Mar 15. PubMed PMID: 30874376.
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