Document detail
ID

oai:pubmedcentral.nih.gov:1066...

Topic
Research
Author
Chen, Chen Liu, Yuanzhen Yao, Jincao Wang, Kai Zhang, Maoliang Shi, Fang Tian, Yuan Gao, Lu Ying, Yajun Pan, Qianmeng Wang, Hui Wu, Jinxin Qi, Xiaoqing Wang, Yifan Xu, Dong
Langue
en
Editor

BioMed Central

Category

BMC Cancer

Year

2023

listing date

12/4/2023

Keywords
benign calcification study performance auroc malignant deep model thyroid nodules radiologists dl
Metrics

Abstract

BACKGROUND: Calcification is a common phenomenon in both benign and malignant thyroid nodules.

However, the clinical significance of calcification remains unclear.

Therefore, we explored a more objective method for distinguishing between benign and malignant thyroid calcified nodules.

METHODS: This retrospective study, conducted at two centers, involved a total of 631 thyroid nodules, all of which were pathologically confirmed.

Ultrasound image sets were employed for analysis.

The primary evaluation index was the area under the receiver-operator characteristic curve (AUROC).

We compared the diagnostic performance of deep learning (DL) methods with that of radiologists and determined whether DL could enhance the diagnostic capabilities of radiologists.

RESULTS: The Xception classification model exhibited the highest performance, achieving an AUROC of up to 0.970, followed by the DenseNet169 model, which attained an AUROC of up to 0.959.

Notably, both DL models outperformed radiologists (P < 0.05).

The success of the Xception model can be attributed to its incorporation of deep separable convolution, which effectively reduces the model’s parameter count.

This feature enables the model to capture features more effectively during the feature extraction process, resulting in superior performance, particularly when dealing with limited data.

CONCLUSIONS: This study conclusively demonstrated that DL outperformed radiologists in differentiating between benign and malignant calcified thyroid nodules.

Additionally, the diagnostic capabilities of radiologists could be enhanced with the aid of DL.

Chen, Chen,Liu, Yuanzhen,Yao, Jincao,Wang, Kai,Zhang, Maoliang,Shi, Fang,Tian, Yuan,Gao, Lu,Ying, Yajun,Pan, Qianmeng,Wang, Hui,Wu, Jinxin,Qi, Xiaoqing,Wang, Yifan,Xu, Dong, 2023, Deep learning approaches for differentiating thyroid nodules with calcification: a two-center study, BioMed Central

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