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. 2024 Mar 29:12:e16952.
doi: 10.7717/peerj.16952. eCollection 2024.

Predicting central cervical lymph node metastasis in papillary thyroid microcarcinoma using deep learning

Yu Wang  1 , Hai-Long Tan  1 , Sai-Li Duan  1 , Ning Li  1 , Lei Ai  1 , Shi Chang  1   2   3
Affiliations

Predicting central cervical lymph node metastasis in papillary thyroid microcarcinoma using deep learning

Yu Wang et al. PeerJ. .

Abstract

Background: The aim of this study is to design a deep learning (DL) model to preoperatively predict the occurrence of central lymph node metastasis (CLNM) in patients with papillary thyroid microcarcinoma (PTMC).

Methods: This research collected preoperative ultrasound (US) images and clinical factors of 611 PTMC patients. The clinical factors were analyzed using multivariate regression. Then, a DL model based on US images and clinical factors was developed to preoperatively predict CLNM. The model's efficacy was evaluated using the receiver operating characteristic (ROC) curve, along with accuracy, sensitivity, specificity, and the F1 score.

Results: The multivariate analysis indicated an independent correlation factors including age ≥55 (OR = 0.309, p < 0.001), tumor diameter (OR = 2.551, p = 0.010), macrocalcifications (OR = 1.832, p = 0.002), and capsular invasion (OR = 1.977, p = 0.005). The suggested DL model utilized US images achieved an average area under the curve (AUC) of 0.65, slightly outperforming the model that employed traditional clinical factors (AUC = 0.64). Nevertheless, the model that incorporated both of them did not enhance prediction accuracy (AUC = 0.63).

Conclusions: The suggested approach offers a reference for the treatment and supervision of PTMC. Among three models used in this study, the deep model relied generally more on image modalities than the data modality of clinic records when making the predictions.

Keywords: Central lymph node metastases; Deep learning; Papillary thyroid microcarcinoma; Ultrasound image.

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Conflict of interest statement

The authors declare there are no competing interests.

Figures

Figure 1
Figure 1. The data screening process.
PTMC, papillary thyroid microcarcinoma; CLNM, central lymph node metastasis; DCNN, deep convolutional neural network; US, ultrasound.
Figure 2
Figure 2. The detailed architecture of the designed DCNN and MLP for images and CFs, respectively.
Figure 3
Figure 3. The flowchart to handle images and CFs, where CFs are represented by digits to add on to the feature map of images.
Figure 4
Figure 4. Comparison of ROC curves in the five-fold cross-validation set by three models. AUC, area under the curve; CFs, clinical factors.
Figure 5
Figure 5. Visualization of network features of seven cases with and without CLNM, respectively.
See this image and copyright information in PMC

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Supplementary concepts