Τρίτη 31 Ιουλίου 2018

Characteristics of the parathyroid gland in endoscopic thyroidectomy with the application of an image enhancement system

Abstract

Background

To assist surgeons in identifying and preserving the parathyroid gland (PTG) in endoscopic thyroidectomy (ET), we have summarized the characteristics of the PTG and the surrounding tissues in ET by applying the Storz Professional Image Enhancement System (SPIES).

Methods

From November 2014 to May 2016, 182 patients with 613 suspected PTGs were included in our study. The shape, color, area, and density of surface blood vessels (SBVs); whether they were encapsulated with adipose tissue; and whether congestion was present during the operation were summarized. The κ coefficient of interobserver agreement in assessing the area and the density of SBVs of suspected PTGs with and without Spectra A (SA) and Spectra B (SB) modalities were calculated. Multiple binary logistic regression analyses were performed to determine the predictive value of different characteristics for detecting the PTG in ET with the application of SPIES.

Results

With visual identification and histopathological results as reference standards, 291 targeted tissues were identified as PTGs, 256 as adipose tissue, 43 as lymph nodes, and 23 as thyroid tissue. The κ coefficients of interobserver agreement in assessing SBV density with or without the SA and SB modalities were 0.944 ± 0.013 and 0.859 ± 0.021, respectively, and those in assessing SBV area were 0.937 ± 0.014 and 0.841 ± 0.022, respectively. In the comparison between PTGs and other tissues, multiple binary logistic regression analysis revealed that shape, color, SBV density, congestion, and whether tissue was encapsulated with adipose tissue were independent predictive factors of PTGs.

Conclusion

With the application of SPIES, the shape, color, density of SBVs, adipose tissue encapsulation, and congestion were independent factors that predicted PTGs in ET. The SA and SB modalities of SPIES could improve the reliability of SBV density and area classifications in targeted tissues.



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