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Objectives: To analyze the performance of radiological assessment categories and quantitative computational analysis of apparent diffusion coefficient (ADC) maps using variant machine learning algorithms to differentiate clinically significant versus insignificant prostate cancer (PCa). Methods: Retrospectively, 73 patients were included in the study. The patients (mean age, 66.3 ± 7.6 years) were examined with multiparametric MRI (mpMRI) prior to radical prostatectomy (n = 33) or targeted biopsy (n = 40). The index lesion was annotated in MRI ADC and the equivalent histologic slides according to the highest Gleason Grade Group (GrG). Volumes of interest (VOIs) were determined for each lesion and normal-appearing peripheral zone. VOIs were processed by radiomic analysis. For the classification of lesions according to their clinical significance (GrG ≥ 3), principal component (PC) analysis, univariate analysis (UA) with consecutive support vector machines, neural networks, and random forest analysis were performed. Results: PC analysis discriminated between benign and malignant prostate tissue. PC evaluation yielded no stratification of PCa lesions according to their clinical significance, but UA revealed differences in clinical assessment categories and radiomic features. We trained three classification models with fifteen feature subsets. We identified a subset of shape features which improved the diagnostic accuracy of the clinical assessment categories (maximum increase in diagnostic accuracy ΔAUC = + 0.05, p < 0.001) while also identifying combinations of features and models which reduced overall accuracy. Conclusions: The impact of radiomic features to differentiate PCa lesions according to their clinical significance remains controversial. It depends on feature selection and the employed machine learning algorithms. It can result in improvement or reduction of diagnostic performance.
Purpose: The Masquelet technique for the treatment of large bone defects is a two-stage procedure based on an induced membrane. Compared to mature periosteum, the induced membrane differs significantly. However, both play a crucial role in bone regeneration. As part of a histological and radiological post-evaluation of an earlier project, we analyzed the influence of the granule size of the bone void filler Herafill® on development of periosteum regrowth in a critical size defect.
Methods: We compared three different sizes of Herafill® granules (Heraeus Medical GmbH, Wehrheim) in vivo in a rat femoral critical size defect (10 mm) treated with the induced membrane technique. After 8 weeks healing time, femurs were harvested and taken for histological and radiological analysis.
Results: A significantly increased regrowth of periosteum into the defect was found when small granules were used. Large granules showed significantly increased occurrence of bone capping. Small granules lead to significant increase in callus formation in the vicinity to the membrane.
Conclusion: The size of Herafill® granules has significant impact on the development of periosteal-like structures around the defect using Masquelet’s induced membrane technique. Small granules show significantly increased regrowth of periosteum and improved bone formation adjacent to the induced membrane.