Parathyroid allotransplantation to treat post-thyroidectomy hypoparathyroidism: A review of case studies.

OBJECTIVE: Symptomatic long-term hypoparathyroidism following thyroid surgery requires an alternative and permanent therapy that would effectively restore parathyroid function and eliminate the need for substitution drug therapy. The aim of this study was to systematically review the literature on the efficacy and safety of parathyroid allotransplantation to treat post-operative hypoparathyroidism. METHODS: MEDLINE, Embase, BIOSIS and the Cochrane Library were searched for published articles (from inception of each database to September 30, 2018). A total of 9 studies comprising 146 patients (177 allotransplantations) with post thyroidectomy hypoparathyroidism were identified. RESULTS: Parathyroid tissues used for allotransplant were cultured parathyroid cells, cryopreserved parathyroid cells and encapsulated microspheres. Post-transplant immunosuppression was only reported in three studies, mainly with oral prednisolone for 2 weeks to 6 months. Mean graft survival following allotransplantation was 47% (95% CI 24%-71%) when patients were followed-up to 6 months and 41% (95% CI 2.3%-80%) at 12 months. There was significant unexplained heterogeneity observed between studies in both these groups (I2 > 50%). Parathyroid hormone (PTH) levels, and serum calcium levels post intervention was not reported in all studies, but available evidence suggests the levels remains higher (PTH level around 12 pg/ml; Ca level around 8 mg/dl) post-allotransplantation for up to 24 months. CONCLUSIONS: Long-term benefit and harms of allotransplantation is still unclear due to the clinical and statistical heterogeneity observed among the studies. Therefore, conduct of a well-designed controlled clinical trial in the immediate future on allotransplantation is of paramount importance.

Using block-based multiparameter representation to detect tumor features on T2-weighted brain MRI images.

OBJECTIVE: The objective of this paper is to present an analytical method for digitized images to detect tumors or lesions in a medical decision support system. METHOD: The authors have developed a simple method of tumor detection using three parameter values: edge (E), gray (G), and contrast (H). The method proposed here first studied the VHD (Visible Human Dataset) input brain feature using EGH parameters that divided the input image into fixed-size blocks (templates). The EGH parameters for the feature blocks were calculated and parameterized to detect the occurrences of abnormalities. These abnormal blocks were then marked for interpretation. RESULTS: Measurements of the following medical dataset were performed: 1) different time-interval images from the same dataset, 2) different brain disease images from multiple datasets, and 3) multiple slice images from multiple datasets. Our experimental results illustrate the ability of our proposed technique to detect tumor blocks with conceptual simplicity and computational efficiency. CONCLUSION: In this paper, we present output examples from our prototype system, comparing detection accuracy and system performance.