Utku Senol

MR Imaging Findings in Brachial Plexopathy with Thoracic Outlet Syndrome

SUMMARY: The BPL is a part of the peripheral nervous system. Many disease processes affect the BPL. In this article, on the basis of 60 patients, we reviewed MR imaging findings of subjects with brachial plexopathy. Different varieties of BPL lesions are discussed.

MRI findings in thoracic outlet syndrome

We discuss MRI findings in patients with thoracic outlet syndrome (TOS). A total of 100 neurovascular bundles were evaluated in the interscalene triangle (IS), costoclavicular (CC), and retropectoralis minor (RPM) spaces. To exclude neurogenic abnormality, MRIs of the cervical spine and brachial plexus (BPL) were obtained in neutral. To exclude compression on neurovascular bundles, sagittal T1W images were obtained vertical to the longitudinal axis of BPL from spinal cord to the medial part of the humerus, in abduction and neutral. To exclude vascular TOS, MR angiography (MRA) and venography (MRV) of the subclavian artery (SA) and vein (SV) in abduction were obtained. If there is compression on the vessels, MRA and MRV of the subclavian vessels were repeated in neutral. Seventy-one neurovascular bundles were found to be abnormal: 16 arterial–venous–neurogenic, 20 neurogenic, 1 arterial, 15 venous, 8 arterial–venous, 3 arterial–neurogenic, and 8 venous–neurogenic TOS. Overall, neurogenic TOS was noted in 69%, venous TOS in 66%, and arterial TOS in 39%. The neurovascular bundle was most commonly compressed in the CC, mostly secondary to position, and very rarely compressed in the RPM. The cause of TOS was congenital bone variations in 36%, congenital fibromuscular anomalies in 11%, and position in 53%. In 5%, there was unilateral brachial plexitis in addition to compression of the neurovascular bundle. Severe cervical spondylosis was noted in 14%, contributing to TOS symptoms. For evaluation of patients with TOS, visualization of the brachial plexus and cervical spine and dynamic evaluation of neurovascular bundles in the cervicothoracobrachial region are mandatory.

Subacute sclerosing panencephalitis: brain stem involvement in a peculiar pattern

Abstract The most common pattern in subacute sclerosing panencephalitis, is in the cerebral hemisphere white matter on T2-weighted images with or without atrophy. Brain-stem lesions are rare. We report brain-stem involvement in two children with subacute sclerosing panencephalitis. A peculiar pattern, with involvement of the pons with extension to both middle cerebellar peduncles and substantia nigra but sparing the pontine tegmentum, is suggested.

Venous drainage patterns in carotid cavernous fistulas.

Purpose. The carotid-cavernous fistula (CCF) is an abnormal arteriovenous communication and its drainage pathways may affect the clinic presentation and change treatment approach. We evaluated drainage patterns of CCFs by digital subtraction angiography (DSA) and categorized drainage pathways according to their types and etiology. Materials and Methods. Venous drainage patterns of 13 CCFs from 10 subjects were studied and categorized as anterior, posterior, superior, inferior, and contralateral on DSA. Drainage patterns were correlated to types and etiology of CCFs. Diagnosis of CCFs was first made by noninvasive imaging techniques. Results. On DSA, traumatic CCFs were usually high flow, direct type while spontaneous CCFs were usually slow flow, indirect type. Bilaterality and mixed types were observed among the indirect spontaneous CCFs. In all CCFs, anterior and inferior drainages were the most common. Contrary to the literature, posterior and superior drainages were noted only in high flow and long standing direct fistulas. Contralateral drainage was not observed in all, supporting plausible compartmentalization of cavernous sinuses. Conclusion. Types, etiology, and duration of the CCFs may affect their drainage patterns. DSA is valuable for categorization of CCFs and verification of drainage patterns. Drainage pathways may affect the clinic presentation and also change treatment approach.

Tigroid pattern on magnetic resonance imaging in Lowe syndrome.

Lowe (oculocerebrorenal) syndrome is an X-linked recessive disorder characterised by congenital cataract, glaucoma, cognitive developmental delay and renal tubular Fanconi syndrome. In this report we present a patient with Lowe syndrome with a tigroid pattern on cranial MRI, which has not been previously reported as an imaging feature of this syndrome.

Automatic abstraction of imaging observations with their characteristics from mammography reports

BACKGROUND Radiology reports are usually narrative, unstructured text, a format which hinders the ability to input report contents into decision support systems. In addition, reports often describe multiple lesions, and it is challenging to automatically extract information on each lesion and its relationships to characteristics, anatomic locations, and other information that describes it. The goal of our work is to develop natural language processing (NLP) methods to recognize each lesion in free-text mammography reports and to extract its corresponding relationships, producing a complete information frame for each lesion. MATERIALS AND METHODS We built an NLP information extraction pipeline in the General Architecture for Text Engineering (GATE) NLP toolkit. Sequential processing modules are executed, producing an output information frame required for a mammography decision support system. Each lesion described in the report is identified by linking it with its anatomic location in the breast. In order to evaluate our system, we selected 300 mammography reports from a hospital report database. RESULTS The gold standard contained 797 lesions, and our system detected 815 lesions (780 true positives, 35 false positives, and 17 false negatives). The precision of detecting all the imaging observations with their modifiers was 94.9, recall was 90.9, and the F measure was 92.8. CONCLUSIONS Our NLP system extracts each imaging observation and its characteristics from mammography reports. Although our application focuses on the domain of mammography, we believe our approach can generalize to other domains and may narrow the gap between unstructured clinical report text and structured information extraction needed for data mining and decision support.