Application of thermography in the diagnostic investigation of thyroid nodules.


Journal

Endocrine journal
ISSN: 1348-4540
Titre abrégé: Endocr J
Pays: Japan
ID NLM: 9313485

Informations de publication

Date de publication:
28 May 2021
Historique:
pubmed: 22 1 2021
medline: 1 12 2021
entrez: 21 1 2021
Statut: ppublish

Résumé

Thyroid nodules (TN) are common in the general population, and the clinical importance of diagnosing thyroid nodules is based on excluding the possibility of thyroid cancer, which occurs in 7-15% of cases. The thyroid gland, owing to its superficial location, is easily accessible via thermography, a noninvasive method of recording body temperature that measures infrared radiation emitted by the body surface. Therefore, this study aimed to evaluate the temperature differences between benign and malignant TN by using thermography. We conducted a cross-sectional study where 147 TN were divided into two groups: the first group included 120 benign nodules and the other included 27 malignant nodules. All the nodules were subjected to ultrasound, fine needle aspiration biopsy, and thermography. On analyzing the thermography results, the benign nodules had a higher temperature at the beginning of the thermography evaluation, and the malignant nodules showed a higher temperature in the middle and at the end (Ft). Using the relationships, it was observed that the temperature delta (ΔT), ΔT nodule/ΔT healthy, ΔT nodule minus ΔT healthy, and nodule Ft minus Ft of the healthy region were higher in malignant nodules. The ROC curve analysis of ΔT demonstrated a cutoff point of 2.38°C, with a sensitivity of 0.963 and specificity of 0.992. Malignant nodules have higher temperatures than benign nodules on thermographic evaluation. This finding suggests that thermography can be a useful tool in the diagnosis of thyroid nodules.

Identifiants

pubmed: 33473070
doi: 10.1507/endocrj.EJ20-0541
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

573-581

Auteurs

Charbel Pereira Damião (CP)

Department of Internal Medicine, Universidade Federal Fluminense (UFF), Niterói, RJ, Brazil.

José Ramón Gonzalez Montero (JRG)

Department of Computer Science, Universidade Federal Fluminense (UFF), Niterói, RJ, Brazil.

Maira Beatriz Hernandez Moran (MBH)

Department of Computer Science, Universidade Federal Fluminense (UFF), Niterói, RJ, Brazil.

Maria Eugênia de Oliveira Marçal E Silva Carvalho (ME)

Department of Internal Medicine, Universidade Federal Fluminense (UFF), Niterói, RJ, Brazil.

Camila Grotta de Farias (CG)

Department of Internal Medicine, Universidade Federal Fluminense (UFF), Niterói, RJ, Brazil.

Izadora Bighetti Brito (IB)

Department of Internal Medicine, Universidade Federal Fluminense (UFF), Niterói, RJ, Brazil.

Maria Auxiliadora Nogueira Saad (MAN)

Department of Internal Medicine, Universidade Federal Fluminense (UFF), Niterói, RJ, Brazil.

Cristina Asvolinsque Pantaleão Fontes (CAP)

Department of Radiology, Universidade Federal Fluminense (UFF), Niterói, RJ, Brazil.

Claudio Fainstein (C)

Department of General and Specialized Surgery, Universidade Federal Fluminense (UFF), Niterói, RJ, Brazil.

Manoel Fernando Oliveira Rodrigues (MFO)

Department of General and Specialized Surgery, Universidade Federal Fluminense (UFF), Niterói, RJ, Brazil.

Aniello Palombo (A)

Department of General and Specialized Surgery, Universidade Federal Fluminense (UFF), Niterói, RJ, Brazil.

Aura Conci (A)

Department of Computer Science, Universidade Federal Fluminense (UFF), Niterói, RJ, Brazil.

Rubens Antunes da Cruz Filho (RA)

Department of Internal Medicine, Universidade Federal Fluminense (UFF), Niterói, RJ, Brazil.

Giovanna Aparecida Balarini Lima (GAB)

Department of Internal Medicine, Universidade Federal Fluminense (UFF), Niterói, RJ, Brazil.

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Classifications MeSH