Spinal stenosis is a condition resulting in the compression of the neural elements due to narrowing of the spinal canal. Anatomical factors including enlargement of the facet joints, thickening of the ligaments, and bulging or collapse of the intervertebral discs contribute to the compression. Decompression surgery alleviates spinal stenosis through a laminectomy involving the resection of bone and ligament. Spinal decompression surgery requires appropriate planning and variable strategies depending on the specific situation. Given the potential for neural complications, there exist significant barriers to residents and fellows obtaining adequate experience performing spinal decompression in the operating room. Virtual teaching tools exist for learning instrumentation which can enhance the quality of orthopaedic training, building competency and procedural understanding. However, virtual simulation tools are lacking for decompression surgery. The aim of this work was to develop an open-source 3D virtual simulator as a teaching tool to improve orthopaedic training in spinal decompression. A custom step-wise spinal decompression simulator workflow was built using 3D Slicer, an open-source software development platform for medical image visualization and processing. The procedural steps include multimodal patient-specific loading and fusion of Computed Tomography (CT) and Magnetic Resonance Imaging (MRI) data, bone threshold-based segmentation, soft tissue segmentation, surgical planning, and a laminectomy and spinal decompression simulation. Fusion of CT and MRI elements was achieved using Fiducial-Based Registration which aligned the scans based on manually placed points allowing for the identification of the relative position of soft and hard tissues. Soft tissue segmentation of the spinal cord, the cerebrospinal fluid, the cauda equina, and the ligamentum flavum was performed using Simple Region Growing Segmentation (with manual adjustment allowed) involving the selection of structures on T1 and/or T2-weighted scans. A high-fidelity 3D model of the bony and soft tissue anatomy was generated with the resulting surgical exposure defined by labeled vertebrae simulating the central surgical incision. Bone and soft tissue resecting tools were developed by customizing manual 3D segmentation tools. Simulating a laminectomy was enabled through bone and ligamentum flavum resection at the site of compression. Elimination of the stenosis enabled decompression of the neural elements simulated by interpolation of the undeformed anatomy above and below the site of compression using Fill Between Slices to reestablish pre-compression neural tissue anatomy. The completed workflow allows patient specific simulation of decompression procedures by staff surgeons, fellows and residents. Qualitatively, good visualization was achieved of merged soft tissue and bony anatomy. Procedural accuracy, the design of resecting tools, and modeling of the impact of bone and ligament removal was found to adequately encompass important challenges in decompression surgery. This software development project has resulted in a well-characterized freely accessible tool for simulating spinal decompression surgery. Future work will integrate and evaluate the simulator within existing orthopaedic resident competency-based curriculum and fellowship training instruction. Best practices for effectively teaching decompression in tight areas of spinal stenosis using virtual simulation will also be investigated in future work

This is quite an innovative study that should lead to a multicentre validation trial. We have developed an FDG-PET/MRI texture-based model for the prediction of lung metastases (LM) in newly diagnosed patients with soft-tissue sarcomas (STSs) using retrospective analysis. In this work, we assess the model performance using a new prospective STS cohort. We also investigate whether incorporating hypoxia and perfusion biomarkers derived from FMISO-PET and DCE-MRI scans can further enhance the predictive power of the model. A total of 66 patients with histologically confirmed STSs were used in this study and divided into two groups: a retrospective cohort of 51 patients (19 LM) used for training the model, and a prospective cohort of 15 patients (two patients with LM, one patient with bone metastases and suspicious lung nodules) for testing the model. In the training phase, a model of four texture features characterising tumour sub-region size and intensity heterogeneities was developed for LM prediction from pre-treatment FDG-PET and MRI scans (T1-weighted, T2-weighted with fat saturation) of the retrospective cohort, using imbalance-adjusted bootstrap statistical resampling and logistic regression multivariable modeling. In the testing phase, this multivariable model was applied to predict the distant metastasis status of the prospective cohort. The predictive power of the obtained model response was assessed using the area under the receiver-operating characteristic curve (AUC). In the exploratory phase of the study, we extracted two heterogeneity metrics from the prospective cohort: the area under the intensity-volume histogram of pre-treatment DCE-MRI volume transfer constant parametric maps and FMISO-PET hypoxia maps (AU-IVH-Ktrans, AU-IVH-FMISO). The impact of the addition of these two individual metrics to the texture-based model response obtained in the testing phase was first investigated using Spearman's correlation (rs), and lastly using logistic regression and leave-one-out cross-validation (LOO-CV) to account for overfitting bias. First, the texture-based model reached an AUC of 0.94, a sensitivity of 1, a specificity of 0.83 and an accuracy of 0.87 when tested in the prospective cohort. In the exploratory phase, the addition of AU-IVH-FMISO did not improve predictive power, yielding a correlation of rs = −0.42 (p = 0.12) with lung metastases, and a relative change in validation AUC of 0% in comparison with the texture-based model response alone in LOO-CV experiments. In contrast, the addition of AU-IVH-Ktrans improved predictive power, yielding a correlation of rs = −0.54 (p = 0.04) with lung metastases, and a change in validation AUC of +10%. Our results demonstrate that texture-based models extracted from pre-treatment FDG-PET and MRI anatomical scans could be successfully used to predict distant metastases in STS cancer. Our results also suggest that the addition of perfusion heterogeneity metrics may contribute to improving model prediction performance

Aims

The aim of this paper was to present the clinical features of patients with musculoskeletal sources of methicillin-sensitive Staphylococcus aureus (MSSA) septicaemia.

A series of 14 patients suffering from tuberculosis of the sternum with a mean follow-up of 2.8 years (2 to 3.6) is presented. All were treated with antitubercular therapy: ten with primary therapy, two needed second-line therapy, and two required surgery (debridement). All showed complete healing and no evidence of recurrence at the last follow-up. MRI was useful in making the diagnosis at an early stage because atypical presentations resulting from HIV have become more common. Early adequate treatment with multidrug antitubercular therapy avoided the need for surgery in 12 of our 14 patients.

Bone-marrow oedema can occur both in isolation and in association with necrosis of bone, but it has not been shown whether each respond to the same methods of treatment.

We treated 16 patients with isolated oedema and 17, in which it was associated with necrosis of the proximal femur, with the prostacyclin derivative iloprost, which has been shown to be effective in the idiopathic form. The Harris hip score, the range of movement, the extent of the oedema as measured by MRI, pain on a visual analogue scale and patient satisfaction were recorded before and subsequent to treatment.

In both groups, we were able to show a significant improvement (p < 0.001) in these observations during the period of follow-up indicating that iloprost will produce clinical improvement in both circumstances.