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. 2022 Sep 5:13:938008.
doi: 10.3389/fendo.2022.938008. eCollection 2022.

Risk factors and diagnostic prediction models for papillary thyroid carcinoma

Xiaowen Zhang  1 , Yuyang Ze  2 , Jianfeng Sang  3 , Xianbiao Shi  3 , Yan Bi  1 , Shanmei Shen  1 , Xinlin Zhang  4 , Dalong Zhu  1
Affiliations

Risk factors and diagnostic prediction models for papillary thyroid carcinoma

Xiaowen Zhang et al. Front Endocrinol (Lausanne). .

Abstract

Thyroid nodules (TNs) represent a common scenario. More accurate pre-operative diagnosis of malignancy has become an overriding concern. This study incorporated demographic, serological, ultrasound, and biopsy data and aimed to compare a new diagnostic prediction model based on Back Propagation Neural Network (BPNN) with multivariate logistic regression model, to guide the decision of surgery. Records of 2,090 patients with TNs who underwent thyroid surgery were retrospectively reviewed. Multivariate logistic regression analysis indicated that Bethesda category (OR=1.90, P<0.001), TIRADS (OR=2.55, P<0.001), age (OR=0.97, P=0.002), nodule size (OR=0.53, P<0.001), and serum levels of Tg (OR=0.994, P=0.004) and HDL-C (OR=0.23, P=0.001) were statistically significant independent differentiators for patients with PTC and benign nodules. Both BPNN and regression models showed good accuracy in differentiating PTC from benign nodules (area under the curve [AUC], 0.948 and 0.924, respectively). Notably, the BPNN model showed a higher specificity (88.3% vs. 73.9%) and negative predictive value (83.7% vs. 45.8%) than the regression model, while the sensitivity (93.1% vs. 93.9%) was similar between two models. Stratified analysis based on Bethesda indeterminate cytology categories showed similar findings. Therefore, BPNN and regression models based on a combination of demographic, serological, ultrasound, and biopsy data, all of which were readily available in routine clinical practice, might help guide the decision of surgery for TNs.

Keywords: Bethesda category; back propagation neural network; diagnostic prediction; logistic regression analysis; papillary thyroid carcinoma.

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Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

Figure 1
Figure 1
Flowchart of patient selection in the study. FTC, follicular thyroid carcinoma; MTC, medullary thyroid cancer.
Figure 2
Figure 2
ROC curve of BPNN and multivariate logistic regression model. BPNN, back propagation neural network; ROC, receiver operating characteristic curve.
See this image and copyright information in PMC

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