Aneurysmal bone cysts rather than local aggressive lesions of the bone which may arise in any part of the axial or appendicular skeleton. Although several theories are available in the literature, the pathogenesis is still conflicting. We report an exceptional case of an aneurysmal bone cyst in the distal femur of a female cerebral palsy patient who underwent bilateral distal femoral derotational osteotomy and plate-screw fixation operations when she was 11 years old. Twenty-four months after the operation, radiographs showed a cystic lesion in the distal portion of the right femur around the osteotomy site. The diagnosis of Aneurysmal Bone Cyst (ABC) was made and the lesion was treated by curettage with cement application. After 36 months of follow-up, there was no recurrence. This is the first case reported in literature which raises the possibility that an osteotomy could be a cause in the development of an aneurysmal bone cyst.
Polyps are well-known cancer precursors identified by colonoscopy. However, variability in their size, appearance, and location makes the detection of polyps challenging. Moreover, colonoscopy surveillance and removal of polyps are highly operator-dependent procedures and occur in a highly complex organ topology. There exists a high missed detection rate and incomplete removal of colonic polyps. To assist in clinical procedures and reduce missed rates, automated methods for detecting and segmenting polyps using machine learning have been achieved in past years. However, the major drawback in most of these methods is their ability to generalise to out-of-sample unseen datasets from different centres, populations, modalities, and acquisition systems. To test this hypothesis rigorously, we, together with expert gastroenterologists, curated a multi-centre and multi-population dataset acquired from six different colonoscopy systems and challenged the computational expert teams to develop robust automated detection and segmentation methods in a crowd-sourcing Endoscopic computer vision challenge. This work put forward rigorous generalisability tests and assesses the usability of devised deep learning methods in dynamic and actual clinical colonoscopy procedures. We analyse the results of four top performing teams for the detection task and five top performing teams for the segmentation task. Our analyses demonstrate that the top-ranking teams concentrated mainly on accuracy over the real-time performance required for clinical applicability. We further dissect the devised methods and provide an experiment-based hypothesis that reveals the need for improved generalisability to tackle diversity present in multi-centre datasets and routine clinical procedures.