Purpose The purpose of this prospective non-randomised study was to compare the efficacy of two opposed methods, operative and conservative. Our hypothesis was that if the method was selected correctly, on an individual basis, the results should be approximately equal. Methods The study included 37 adolescents aged between 12 and 16 years, with a mean follow up of 6.1 years. The presence of a significant loose body, confirmed by precise imaging, was the key for selecting operative or arthroscopic treatment. In both groups of patients, we evaluated functional knee scores and the incidence of residual patellofemoral disorders.Results We confirmed our hypothesis using the t-test to compare functional results and a test for comparison of proportions for incidence of residual disorders. There was no statistically significant difference (p=0.091) between operatively and conservatively treated groups with regard to functional results. The same statistical outcome emerged when comparing incidences of re-dislocation (p=0.854), or other major patellar instabilities (p=0.856), between the groups. Conclusions The results obtained should not promote a non-operative method on the basis of lower risk, but do support an individual approach based on precise diagnosis and defined criteria.

The prediction of induction chemotherapy response at the time of diagnosis may improve outcomes in osteosarcoma by allowing for personalized tailoring of therapy. The aim of this study was thus to investigate the predictive potential of the so far unexploited computational analysis of osteosarcoma magnetic resonance (MR) images. Fractal and gray level cooccurrence matrix (GLCM) algorithms were employed in retrospective analysis of MR images of primary osteosarcoma localized in distal femur prior to the OsteoSa induction chemotherapy. The predicted and actual chemotherapy response outcomes were then compared by means of receiver operating characteristic (ROC) analysis and accuracy calculation. Dbin, Λ, and SCN were the standard fractal and GLCM features which significantly associated with the chemotherapy outcome, but only in one of the analyzed planes. Our newly developed normalized fractal dimension, called the space-filling ratio (SFR) exerted an independent and much better predictive value with the prediction significance accomplished in two of the three imaging planes, with accuracy of 82% and area under the ROC curve of 0.20 (95% confidence interval 0–0.41). In conclusion, SFR as the newly designed fractal coefficient provided superior predictive performance in comparison to standard image analysis features, presumably by compensating for the tumor size variation in MR images.

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