Impact of microscopic extrathyroidal extension on differentiated thyroid cancer post-surgical risk of recurrence: a retrospective analysis.

J Endocrinol Invest

Endocrinology Unit, Department of Internal Medicine and Medical Specialties (DIMI), University of Genova, Viale Benedetto XV, 6, 16132, Genoa, Italy.

Published: October 2023

Purpose: In the last edition of the American Joint Committee on Cancer (AJCC) staging system, differentiated thyroid cancers (DTC) showing microscopic extrathyroidal extension (mETE) are considered comparable to intrathyroidal cancers for their clinical behavior and prognosis. The aim of the study is to evaluate the impact of this updated assessment of T, when applied to the postoperative recurrence risk stratification, according to the American Thyroid Association Guidelines (ATA-RR).

Methods: One-hundred DTC patients who underwent total thyroidectomy were retrospectively evaluated. The downstaging of mETE was introduced in the definition of T, and the updated classification defined as modified ATA-RR (ATAm-RR). For each patient, post-surgical basal and stimulated thyroglobulin (Tg) levels, neck ultrasound (US) and post-ablative 131-I whole body scan (WBS) reports were considered. The predictive performance (PP) of disease recurrence was calculated both for each single parameter, as well as for all of them.

Results: According to ATAm-RR classification, 19/100 patients (19%) were downstaged. ATA-RR proved a significant PP for disease recurrence (DR) (sensitivity 75.0%, specificity 63.0%, p = 0.023). However, ATAm-RR performed slightly better due to an increased specificity (sensitivity 75.0%, specificity 83.7%, p < 0.001). For both classifications, the PP was optimal when all the above-mentioned predictive parameters were considered.

Conclusion: Our results suggest that the new assessment of T considering mETE resulted in a downgrading of ATA-RR class in a significant number of patients. This provides a better PP for disease recurrence, and the best PP was obtained when considering the whole predictive variables together.

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC10514175PMC
http://dx.doi.org/10.1007/s40618-023-02070-yDOI Listing

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