Intraosseous ganglion cyst mimicking chondrosarcoma on MRI: a case report

Background The intraosseous ganglia is a benign cyst, rarely locate in the olecranon process. As intraosseous ganglia can mimic malignant bone tumor, computed tomography (CT) is important for diagnosis even when magnetic resonance imaging (MRI) suggests malignant bone tumor, such as chondrosarcoma. Case presentation In this paper, we report a 42-year-old woman with intraosseous ganglia in the olecranon process of the ulna. She complained pain in right elbow for 3 weeks. MRI revealed an intraosseous mass which initially diagnosed as chondrosarcoma. However, followed computed tomography (CT) demonstrated scattered intralesional gas and no underlying mineralization, and we can exclude chondrosarcoma from diagnosis. Conclusions The intraosseous ganglia can mimic bone tumor in MRI; therefore, CT is essential for accurate characterization of bone tumor. Even if MR imaging strongly suggests chondrosarcoma of the bone, CT should be performed as additional study.

Efficient Fuzzy Image Stretching for Automatic Ganglion Cyst Extraction Using Fuzzy C-Means Quantization

Ganglion cysts are commonly observed in association with the joints and tendons of the appendicular skeleton. Ultrasonography is the favored modality used to manage such benign tumors, but it may suffer from operator subjectivity. In the treatment phase, ultrasonography also provides guidance for aspiration and injection, and the information regarding the accurate location of the pedicle of the ganglion. Thus, in this paper, we propose an automatic ganglion cyst extracting method based on fuzzy stretching and fuzzy C-means quantization. The proposed method, with its carefully designed image-enhancement policy, successfully detects ganglion cysts in 86 out of 90 cases (95.6%) without requiring human intervention.

Intelligent Automatic Segmentation of Wrist Ganglion Cysts Using DBSCAN and Fuzzy C-Means

Ganglion cysts are common soft tissue masses of the hand and wrist, and small size cysts are often hypoechoic. Thus, identifying them from ultrasonography is not an easy problem. In this paper, we propose an automatic segmentation method using two artificial intelligence algorithms in sequence. A density based unsupervised learning algorithm called DBSCAN is performed as a front-end and its result determines the number of clusters used in the Fuzzy C-Means (FCM) clustering algorithm for quantification of ganglion cyst object. In an experiment using 120 images, the proposed method shows a higher extraction rate (89.2%) and lower false positive rate compared with FCM when the ground truth is set as the human expert’s decision. Such human-like behavior is more apparent when the size of the ganglion cyst is small that the quality of ultrasonography is often not very high. With this fully automatic segmentation method, the operator subjectivity that is highly dependent on the experience of the ultrasound examiner can be mitigated with high reliability.

A Case Series of Intraneural Ganglion Cyst Involving the Common Peroneal Nerve: Potential Cause for Foot Drop

Background: Intraneural ganglion cysts (IGC) are non-neoplastic mucinous cysts that form when thick mucinous fluid accumulates in the epineurium of peripheral nerves, with the majority of cases occurring in the para-articular or articular areas. Case Presentation: The two cases of a 69-year-old man and a 59-year-old man who acquired peroneal nerve neuropathy as a result of an intraneural ganglion cyst are presented here. The L.L.R.M. Medical College Department of Orthopedics provided care for them. There was complete nerve recovery in all patients after substantial cyst wall dissection and secretion removal. Conclusion: An intraneural ganglion cyst and trauma may exacerbate nerve damage, despite the fact that it is difficult to detect the cyst before surgery. An early diagnosis and prophylactic actions are typically associated with better outcomes. KEY WORDS: Intraneural ganglion, Common peroneal nerve, Foot drop.