Background The coexistence of hyperparathyroidism and thyroid cancer presents important diagnostic and management challenges. With minimally invasive parathyroid surgery trending, preoperative thyroid imaging becomes more important as concomitant thyroid and parathyroid lesions are reported. The aim of the study was to evaluate the rate of thyroid cancer in patients operated for either primary (PHPT) or secondary hyperparathyroidism (SHPT). Methods Our retrospective study included PHPT and SHPT patients submitted to parathyroidectomy and, when indicated, concomitant thyroid surgery between 2010 and 2017. Results Parathyroidectomy was performed in 217 patients: 140 (64.5%) for PHPT and 77 (35.5%) for SHPT. Concomitant thyroid surgery was performed in 75 patients with PHPT (53.6%), and 19 papillary thyroid carcinomas (PTC) were found, accounting for 13.6% from all cases with PHPT and 25.3% from PHPT cases with concomitant thyroid surgery. Thirty-one of operated SHPT patients (40.3%) also underwent thyroid surgery and 9 PTC cases were diagnosed (11.7% of all SHPT patients and 29% of patients with concomitant thyroid surgery). We found differences between PHPT and SHPT patients ( p < 0.001) with respect to age (54.6 ± 13y versus 48.8 ± 12y), female-to-male ratio (8:1 versus ~ 1:1), surgical technique (single gland parathyroidectomy in 82.8% PHPT cases; versus subtotal parathyroidectomy in 85.7% SHPT cases) and presurgical PTH (357.51 ± 38.11 pg/ml versus 1020 ± 161.38 pg/ml). Morphopathological particularities, TNM classification and multifocality incidence of PTC were similar in the two groups. All PTC from patients with SHPT were thyroid microcarcinomas (TMC, i.e. tumors with a diameter smaller than 1 cm), whereas seven out of the 19 cases with PTC and PHPT were larger than 1 cm. Conclusions PTC was frequently and similarly associated with both PHPT and SHPT irrespective of presurgical PTH levels. Thyroid tumors above 1 cm were found only in patients with PHPT. Investigators should focus also on associated thyroid nodular pathology in patients with PHPT.
Purpose. To correlate the volume of parathyroid adenomas with the hormonal and metabolic profile at patients diagnosed with primary hyperparathyroidism (pHPTH). Patients and Methods. Cross-sectional multicentric study, enrolling 52 patients with pHPTH from two medical institutions. Serum calcium and PTH were evaluated in all patients before surgery, whereas 25OHD3 was measured only in the 33 patients recruited form one medical unit. The volume of parathyroid adenoma was measured by using the formula of a rotating ellipsoid. Results. We observed a significant correlation of the volume of parathyroid adenomas with PTH at patients from the two units and in the whole group (p < 0.0001), but not with serum calcium (p = 0.494). Twenty-five out of the 33 patients at whom 25OHD3 was measured had levels in the range of deficiency. 25OHD3 was not correlated with PTH or calcium levels, but was negatively correlated to the adenoma volume and positively to the PTH/volume ratio (p = 0.041 and p = 0.048, respectively). Conclusions. The volume of parathyroid adenoma seems to be related to preoperative PTH and 25OHD3, but not to calcium level. Vitamin D deficiency is frequently found at patients with pHPTH and may contribute to particular disease profiles, including larger parathyroid adenomas.
Deep learning (DL) has seen an exponential development in recent years, with major impact in many medical fields, especially in the field of medical image. The purpose of the work converges in determining the importance of each component, describing the specificity and correlations of these elements involved in achieving the precision of interpretation of medical images using DL. The major contribution of this work is primarily to the updated characterisation of the characteristics of the constituent elements of the deep learning process, scientific data, methods of knowledge incorporation, DL models according to the objectives for which they were designed and the presentation of medical applications in accordance with these tasks. Secondly, it describes the specific correlations between the quality, type and volume of data, the deep learning patterns used in the interpretation of diagnostic medical images and their applications in medicine. Finally presents problems and directions of future research. Data quality and volume, annotations and labels, identification and automatic extraction of specific medical terms can help deep learning models perform image analysis tasks. Moreover, the development of models capable of extracting unattended features and easily incorporated into the architecture of DL networks and the development of techniques to search for a certain network architecture according to the objectives set lead to performance in the interpretation of medical images.
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