Cargando…
Dynamic Risk Stratification for Predicting Treatment Response in Differentiated Thyroid Cancer
Prognosis in Differentiated Thyroid Cancer (DTC) patients is excellent, but a significant degree of overtreatment still exists because of the inability to accurately identify small patient cohorts who experience a more aggressive form of the disease, often associated with certain poor prognostic fac...
Autores principales: | , , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2020
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7565378/ https://www.ncbi.nlm.nih.gov/pubmed/32825789 http://dx.doi.org/10.3390/jcm9092708 |
_version_ | 1783595918991294464 |
---|---|
author | Giannoula, Evanthia Melidis, Christos Papadopoulos, Nikitas Bamidis, Panagiotis Raftopoulos, Vasilios Iakovou, Ioannis |
author_facet | Giannoula, Evanthia Melidis, Christos Papadopoulos, Nikitas Bamidis, Panagiotis Raftopoulos, Vasilios Iakovou, Ioannis |
author_sort | Giannoula, Evanthia |
collection | PubMed |
description | Prognosis in Differentiated Thyroid Cancer (DTC) patients is excellent, but a significant degree of overtreatment still exists because of the inability to accurately identify small patient cohorts who experience a more aggressive form of the disease, often associated with certain poor prognostic factors. Identifying these cohorts at an early stage would allow patients at high risk to receive more aggressive treatment while avoiding unnecessary and invasive treatments in those at low risk. Most risk stratification systems include age, tumor size, grade, presence of local invasion, and regional or distant metastases. Here we discuss these common factors as well as their association with treatment response, but also other upcoming markers including histology and multifocality of primary tumor, dose administered and preparation method for Radioiodine Therapy (RAI), Thyroglobulin (Tg), Anti-thyroglobulin Antibodies (Tg-Ab) levels both at initial management and during follow-up, and the presence of previously existing benign thyroid disease. In addition, we examine the role of remnant size and avidity as well as surgeons’ experience in performing thyroidectomies with recurrence rate, discussing its impact on disease prognosis. Our results reveal that treatment response has a statistically significant association with histology, T and M stages, surgeons’ experience, Tg levels and remnant score both during RAI and follow up and Tg-Ab levels during follow-up whole body scan (WBS). |
format | Online Article Text |
id | pubmed-7565378 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-75653782020-10-26 Dynamic Risk Stratification for Predicting Treatment Response in Differentiated Thyroid Cancer Giannoula, Evanthia Melidis, Christos Papadopoulos, Nikitas Bamidis, Panagiotis Raftopoulos, Vasilios Iakovou, Ioannis J Clin Med Article Prognosis in Differentiated Thyroid Cancer (DTC) patients is excellent, but a significant degree of overtreatment still exists because of the inability to accurately identify small patient cohorts who experience a more aggressive form of the disease, often associated with certain poor prognostic factors. Identifying these cohorts at an early stage would allow patients at high risk to receive more aggressive treatment while avoiding unnecessary and invasive treatments in those at low risk. Most risk stratification systems include age, tumor size, grade, presence of local invasion, and regional or distant metastases. Here we discuss these common factors as well as their association with treatment response, but also other upcoming markers including histology and multifocality of primary tumor, dose administered and preparation method for Radioiodine Therapy (RAI), Thyroglobulin (Tg), Anti-thyroglobulin Antibodies (Tg-Ab) levels both at initial management and during follow-up, and the presence of previously existing benign thyroid disease. In addition, we examine the role of remnant size and avidity as well as surgeons’ experience in performing thyroidectomies with recurrence rate, discussing its impact on disease prognosis. Our results reveal that treatment response has a statistically significant association with histology, T and M stages, surgeons’ experience, Tg levels and remnant score both during RAI and follow up and Tg-Ab levels during follow-up whole body scan (WBS). MDPI 2020-08-21 /pmc/articles/PMC7565378/ /pubmed/32825789 http://dx.doi.org/10.3390/jcm9092708 Text en © 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Giannoula, Evanthia Melidis, Christos Papadopoulos, Nikitas Bamidis, Panagiotis Raftopoulos, Vasilios Iakovou, Ioannis Dynamic Risk Stratification for Predicting Treatment Response in Differentiated Thyroid Cancer |
title | Dynamic Risk Stratification for Predicting Treatment Response in Differentiated Thyroid Cancer |
title_full | Dynamic Risk Stratification for Predicting Treatment Response in Differentiated Thyroid Cancer |
title_fullStr | Dynamic Risk Stratification for Predicting Treatment Response in Differentiated Thyroid Cancer |
title_full_unstemmed | Dynamic Risk Stratification for Predicting Treatment Response in Differentiated Thyroid Cancer |
title_short | Dynamic Risk Stratification for Predicting Treatment Response in Differentiated Thyroid Cancer |
title_sort | dynamic risk stratification for predicting treatment response in differentiated thyroid cancer |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7565378/ https://www.ncbi.nlm.nih.gov/pubmed/32825789 http://dx.doi.org/10.3390/jcm9092708 |
work_keys_str_mv | AT giannoulaevanthia dynamicriskstratificationforpredictingtreatmentresponseindifferentiatedthyroidcancer AT melidischristos dynamicriskstratificationforpredictingtreatmentresponseindifferentiatedthyroidcancer AT papadopoulosnikitas dynamicriskstratificationforpredictingtreatmentresponseindifferentiatedthyroidcancer AT bamidispanagiotis dynamicriskstratificationforpredictingtreatmentresponseindifferentiatedthyroidcancer AT raftopoulosvasilios dynamicriskstratificationforpredictingtreatmentresponseindifferentiatedthyroidcancer AT iakovouioannis dynamicriskstratificationforpredictingtreatmentresponseindifferentiatedthyroidcancer |