The prevalence and morphometric features of mastoid emissary vein on multidetector computed tomography

Background: The aim of the study was to evaluate the prevalence and morphometric features of mastoid emissary vein (MEV) on multidetector computed tomography (MDCT) scans, emphasize its clinical significance and review its surgical implications. Materials and methods: Cranial and temporal bone MDCTs of 248 patients (496 sides) were analysed by 2 radiologists. Mastoid foramen (MF) was defined on the 3 dimensional volume rendered (3DVR) images. The MF and mastoid emissary canal (MEC) were investigated in axial thin slices and the diameters of the largest MF and MEC were measured. Mean diameters of MF and MEC were determined. The number of the mastoid foramina was noted. Differences in MF prevalence by sex and side were evaluated. Results: The overall prevalence of MEC was 92.3%. It was observed in 91.5% of women and 93.3% of men. MEC was present on the right side in 84.7% and on the left side in 82.3% of temporal bones. The mean diameter of MF was 1.92 ± ± 1.02 mm on the right and 1.84 ± 0.98 mm on the left. In both sides the number of the MF’s changed from absent to triple. The mean diameter of MEC was 1.58 ± 0.86 mm on the right and 1.48 ± 0.79 mm on the left side. The mean diameter of MEC was significantly larger in men. No significant correlation was detected between age and the MEC diameter. Conclusions: The preoperative detection of mastoid emissary veins is necessary. The radiologists should be familiar with their clinical significance and variant appearances and report them accurately. Knowledge of their morphology and surgical implications by the surgeons will make them aware to avoid unexpected and fatal complications while operating in the suboccipital and mastoid area. MDCT is a reliable diagnostic tool for imaging the MEC and MF.

MR imaging of pachydermoperiostosis

WOS: 000080401600008PubMed ID: 10363444A case of pachydermoperiostosis who demonstrated the whole syndrome (pachyderma, periostitis, and cutis verticis gyrata) is presented, and the Magnetic Resonance Imaging (MRI) appearances of the long bone and scalp changes are demonstrated. MRI of the cruris demonstrated fluffy periosteal new bone formation that encroached on the medullary cavity as well as expansion of the diaphysis. Cranial changes included thickening of the diploe associated with diminished signal of the intradiploic fat, and thickening of the scalp with furrowing

The use of radiomics and machine learning for the differentiation of chondrosarcoma from enchondroma

Purpose: To construct and compare machine learning models for differentiating chondrosarcoma from enchondroma using radiomic features from T1 and fat suppressed Proton density (PD) magnetic resonance imaging (MRI). Methods: Eighty-eight patients (57 with enchondroma, 31 with chondrosarcoma) were retrospectively included. Histogram matching and N4ITK MRI bias correction filters were applied. An experienced musculoskeletal radiologist and a senior resident in radiology performed manual segmentation. Voxel sizes were resampled. Laplacian of Gaussian filter and wavelet-based features were used. One thousand eight hundred eighty-eight features were obtained for each patient, with 944 from T1 and 944 from PD images. Sixty-four unstable features were removed. Seven machine learning models were used for classification. Results: Classification with all features showed neural network was the best model for both readers' datasets with area under the curve (AUC), classification accuracy (CA), and F1 score of 0.979, 0.984; 0.920, 0.932; and 0.889, 0.903, respectively. Four features, including one common to both readers, were selected using fast correlation based filter. The best performing models with selected features were gradient boosting for Fatih Erdem's dataset and neural network for Gülen Demirpolat's dataset with AUC, CA, and F1 score of 0.990, 0.979; 0.943, 0.955; 0.921, 0.933, respectively. Neural Network was the second-best model for FE's dataset based on AUC (0.984). Conclusion: Using pathology as a gold standard, this study defined and compared seven well-performing models to distinguish enchondromas from chondrosarcomas and provided radiomic feature stability and reproducibility among the readers. © 2023 Wiley Periodicals LLC