Download This Paper Open PDF in Browser Add Paper to My Library Share: Permalink Using these links will ensure access to this page indefinitely Copy URL Copy DOI
Objective To clarify whether bone marrow edema is detectable on initial MR imaging of nontraumatic femoral head necrosis,and to discover the pathogenesis of bone marrow edema. Methods Eighty-two patients (132 hips) with known osteonecrosis were examined consecutively with MRI,CT and plain film. Forty five of the 132 hips with marrow edema on MRI were recruited. Eight resected specimens among them were studied by pathology and correlated with preoperative MRI with emphasis on the histopathological basis of marrow edema. Results Of the 132 hips,37 hips with bone marrow edema were observed in articular collapse. Bone marrow edema was more present in the collapse of the femoral head than none( P 0.001). Eight core biopsies were performed during surgery on two right and six left hips,consistent with bone marrow edema on MRI. All histologic sections in the bone marrow edema area showed exudates and fibroblastic proliferation in the medullary spaces. Conclusion Bone marrow edema means the later pathological phase of osteonecrosis. It should be considered as a marker for potential progression to advanced osteonecrosis and careful examinations are necessary when bone marrow is seen.
Abstract Background: Preoperative prediction of the soft tissue sarcomas (STSs) grade is important for treatment decisions. To preoperatively distinguish low-grade (grades I and II) and high-grade (grade III) STSs, we developed and validated the performance of a magnetic resonance imaging (MRI)-based radiomics nomogram. Methods: Patients with an STS based on the French Federation of Cancer Centers Sarcoma Group grading system at two independent institutions were enrolled (training set, n = 109; external validation set, n = 71). The minimum redundancy maximum relevance method and least absolute shrinkage and selection operator logistic regression were used to process feature selection and radiomics signature development. Three radiomics signature models were constructed based on T1-weighted imaging (RS-T1 model) and fat-suppressed T2-weighted imaging sequences (RS-FST2 model) and their combination (RS-Combined model). Model performance (discrimination capability, calibration curve, and clinical usefulness) was evaluated in the external validation set. Results: The RS-T1 model, RS-FST2 model, and RS-Combined model achieved predictive abilities with area under the receiver operating characteristic curves (AUCs) of 0.645, 0.641, and 0.829, respectively, in the external validation set. The nomogram, incorporating significant clinical factors and the RS-Combined model, showed extremely high predictive ability in the training set and external validation set with AUCs of 0.916 (95% confidence interval, 0.866–0.966) and 0.879 (0.791–0.967), respectively. The nomogram achieved significant patient stratification. Conclusions: The proposed noninvasive MRI-based radiomics nomogram shows superior predictive performance in differentiating low-grade from high-grade STS.
Background. The prognosis of IDH1-mutant glioma is significantly better than that of wild-type glioma, and the preoperative identification of IDH mutations in glioma is essential for the formulation of surgical procedures and prognostic assessment. Purpose. To explore the value of a radiomic model based on preoperative-enhanced MR images in the assessment of the IDH1 genotype in high-grade glioma. Materials and Methods. A retrospective analysis was performed on 182 patients with high-grade glioma confirmed by surgical pathology between December 2012 and January 2019 in our hospital with complete preoperative brain-enhanced MR images, including 79 patients with an IDH1 mutation (45 patients with WHO grade III and 34 patients with WHO grade IV) and 103 patients with wild-type IDH1 (33 patients with WHO grade III and 70 patients with WHO grade IV). Patients were divided into a primary dataset and a validation dataset at a ratio of 7 : 3 using a stratified random sampling; radiomic features were extracted using A.K. (Analysis Kit, GE Healthcare) software and were initially reduced using the Kruskal-Wallis and Spearman analyses. Lasso was finally conducted to obtain the optimized subset of the feature to build the radiomic model, and the model was then tested with cross-validation. ROC (receiver operating characteristic curve) analysis was performed to evaluate the performance of the model. Results. The radiomic model showed good discrimination in both the primary dataset ( , 95% CI: 0.754 to 0.855, , , , , and ) and the validation dataset ( , 95% CI: 0.690 to 0.913, , , , , and ). Conclusion. The radiomic model, based on the preoperative-enhanced MR, can effectively predict the IDH1 genotype in high-grade glioma.
Objective To evaluate the protective effect of dental adhesive resin cement on dentin surface afterpreparation for all-ceramic crown.Methods 63 patients with 82 teeth prepared for all-ceramic crown were divided intogroups A and B in time sequence.Group A ( 32 patients,42 teeth )was applied with dental adhesive resin cement and group B ( 31 patients,40 teeth )was applied with Gluma desensitizer.Visual analog scale ( VAS )was used to assess th e sensitivity degree of the prepared teeth using tactile test and air blast test.The results were analyzed using a statistics software.Results In groups A and B,significant differences were found between the baseline VAS value of tactile test and air blast test ( pre-treatment ) and the value gained after both tests ( P＜ 0.05 ).No significant difference was fo und between group A and group B in the VAS value ( P＞ 0.05 ).Conclusions Dental adhesive resin cement can markedlyrelieve the symptoms of dentin hypersensitivity after dental preparation of all-ceramic crown.
Key words:
Dental adhesive resin cement; Gluma desensitizer; Dentin hypersensitivity; Dental preparation
'/%3e%3cuse xlink:href='%23a' transform='rotate(23.036 .093 25.536)'/%3e%3cuse xlink:href='%23a' transform='rotate(45.87 1.273 16.18)'/%3e%3cuse xlink:href='%23a' transform='rotate(69.945 .996 12.078)'/%3e%3cuse xlink:href='%23a' transform='rotate(20.66 -19.689 31.932)'/%3e%3c/svg%3e)