Anterior shoulder instability is associated with osseous defects of the glenoid and/or humeral head (Hill-Sachs lesions). These defects can contribute to the pathology of instability by engaging together. There is a need to continue to develop methods to preoperatively identify engaging Hill-Sachs lesions for determining appropriate surgical management.

The objective was to created a working moveable 3D CT model that allows the user to move the shoulder joint into various positions to assess the relationship between the Hill-Sachs lesion and the anterior glenoid rim. This technique was applied to a cohort series of 14 patients with recurrent anterior dislocation: 4 patients had undergone osteoarticular allografting of Hill-Sachs lesions and 10 control patients had undergone CT scanning to quantify bone loss but had no treatment to address bony pathology. A biomechanical analysis was performed to rotate each 3D model using local coordinate systems through a functional range using an open-source 3D animation program, Blender (Amsterdam, Netherlands). A Hill-Sachs lesion was considered “dynamically” engaging if the angle between the lesion's long axis and anterior glenoid was parallel.

In the classical vulnerable position of the shoulder (abduction=90, external rotation=0–135), none of the Hill-Sachs lesions aligned with the anterior glenoid in any of our patients (Figure 1). Therefore, we considered there to be a “low risk” of engagement in these critical positions, as the non-parallel orientation represents a lack of true articular arc mismatch and is unlikely to produce joint instability. We then expanded our search and simulated shoulder positions throughout a physiological range of motion for all groups and found that 100% of the allograft patients and 70% of the controls had positions producing alignment and were “high risk” of engagement (p = 0.18) (Table 1). We also found that the allograft group had a greater number of positions that would engage (mean 4 ± 1 positions of engagement) compared to our controls (mean 2 ± 2 positions of engagement, p = 0.06).

We developed a 3D animated paradigm to dynamically and non-invasively visualize a patient's anatomy and determine the clinical significance of a Hill-Sachs lesion using open source software and CT images. The technique demonstrated in this series of patients showed multiple shoulder positions that align the Hill-Sachs and glenoid axes that do not necessarily meet the traditional definition of engagement. Identifying all shoulder positions at risk of “engaging”, in a broader physiological range, may have critical implications towards selecting the appropriate surgical management of bony defects. We do not claim to doubt the classic conceptual definition of engagement, but we merely introduce a technique that accounts for the dynamic component of shoulder motion, and in doing so, avoid limitations of a static criteria assumed traditional definition (like size and location of lesion). Further investigations are planned and will help to further validate the clinical utility of this method.

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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.