Cordes M, Götz TI, Lang EW, Coerper S, Kuwert T, Schmidkonz C. Advanced thyroid carcinomas: neural network analysis of ultrasonographic characteristics.
Thyroid Res 2021;
14:16. [PMID:
34187534 PMCID:
PMC8240264 DOI:
10.1186/s13044-021-00107-z]
[Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/03/2021] [Accepted: 06/21/2021] [Indexed: 12/20/2022] Open
Abstract
BACKGROUND
Ultrasound is the first-line imaging modality for detection and classification of thyroid nodules. Certain characteristics observable by ultrasound have recently been identified that may indicate malignancy. This retrospective cohort study was conducted to test the hypothesis that advanced thyroid carcinomas show distinctive clinical and sonographic characteristics. Using a neural network model as proof of concept, nine clinical/sonographic features served as input.
METHODS
All 96 study enrollees had histologically confirmed thyroid carcinomas, categorized (n = 32, each) as follows: group 1, advanced carcinoma (ADV) marked by local invasion or distant metastasis; group 2, non-advanced papillary carcinoma (PTC); or group 3, non-advanced follicular carcinoma (FTC). Preoperative ultrasound profiles were obtained via standardized protocols. The neural network had nine input neurons and one hidden layer.
RESULTS
Mean age and the number of male patients in group 1 were significantly higher compared with groups 2 (p = 0.005) or 3 (p < 0.001). On ultrasound, tumors of larger volume and irregular shape were observed significantly more often in group 1 compared with groups 2 (p < 0.001) or 3 (p ≤ 0.01). Network accuracy in discriminating advanced vs. non-advanced tumors was 84.4% (95% confidence interval [CI]: 75.5-91), with positive and negative predictive values of 87.1% (95% CI: 70.2-96.4) and 92.3% (95% CI: 83.0-97.5), respectively.
CONCLUSIONS
Our study has shown some evidence that advanced thyroid tumors demonstrate distinctive clinical and sonographic characteristics. Further prospective investigations with larger numbers of patients and multicenter design should be carried out to show whether a neural network incorporating these features may be an asset, helping to classify malignancies of the thyroid gland.
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