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. 2024 Feb 29:2024:3711123.
doi: 10.1155/2024/3711123. eCollection 2024.

A Nomogram to Predict Benign/Malignant Mediastinal Lymph Nodes Based on EBUS Sonographic Features

Bingchao Ling  1 , Weishun Xie  1 , Yi Zhong  1 , Taowen Feng  1 , Yueli Huang  1 , Lianying Ge  1 , Aiqun Liu  1
Affiliations

A Nomogram to Predict Benign/Malignant Mediastinal Lymph Nodes Based on EBUS Sonographic Features

Bingchao Ling et al. Int J Clin Pract. .

Abstract

Background: Endobronchial ultrasound (EBUS) sonographic features help identify benign/malignant lymph nodes while conducting transbronchial needle aspiration (TBNA). This study aims to identify risk factors for malignancy based on EBUS sonographic features and to estimate the risk of malignancy in lymph nodes by constructing a nomogram.

Methods: 1082 lymph nodes from 625 patients were randomly enrolled in training (n = 760) and validation (n = 322) sets. The subgroup of EBUS-TBNA postoperative negative lymph nodes (n = 317) was randomly enrolled in a training (n = 224) set and a validation (n = 93) set. Logistic regression analysis was used to identify the EBUS features of malignant lymph nodes. A nomogram was formulated using the EBUS features in the training set and later validated in the validation set.

Results: Multivariate analysis revealed that long-axis, short-axis, echogenicity, fusion, and central hilar structure (CHS) were the independent predictors of malignant lymph nodes. Based on these risk factors, a nomogram was constructed. Both the training and validation sets of 5 EBUS features nomogram showed good discrimination, with area under the curve values of 0.880 (sensitivity = 0.829 and specificity = 0.807) and 0.905 (sensitivity = 0.819 and specificity = 0.857). Subgroup multivariate analysis revealed that long-axis, echogenicity, and CHS were the independent predictors of malignancy outcomes of EBUS-TBNA postoperative negative lymph nodes. Based on these risk factors, a nomogram was constructed. Both the training and validation sets of 3 EBUS features nomogram showed good discrimination, with the area under the curve values of 0.890 (sensitivity = 0.882 and specificity = 0.786) and 0.834 (sensitivity = 0.930 and specificity = 0.636).

Conclusions: Our novel scoring system based on two nomograms can be utilized to predict malignant lymph nodes.

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Conflict of interest statement

The authors declare that they have no conflicts of interest.

Figures

Figure 1
Figure 1
Representative morphology of EBUS features.
Figure 2
Figure 2
Flowchart of nomogram development and verification.
Figure 3
Figure 3
Developed 5 EBUS features nomogram with the following features: long-axis, short-axis, echogenicity, fusion, and CHS. CHS, central hilar structure.
Figure 4
Figure 4
The performance of 5 EBUS features nomogram in the training set and the validation set. (a) ROC curve of 5 EBUS features nomogram for predicting malignant lymph nodes in the training dataset. (b) ROC curve of 5 EBUS features nomogram for predicting malignant lymph nodes in the validation dataset. (c) Calibration curve of 5 EBUS features nomogram for predicting malignant lymph nodes in the training dataset. (d) Calibration curve of 5 EBUS features nomogram for predicting malignant lymph nodes in the validation dataset.
Figure 5
Figure 5
DCA and CIC of 5 EBUS features nomogram in the training set and the validation set. (a) DCA of 5 EBUS features nomogram for predicting malignant lymph nodes in the training set. (b) DCA of 5 EBUS features nomogram for predicting malignant lymph nodes in the validation set. (c) CIC of 5 EBUS features nomogram for predicting malignant lymph nodes in the training set. (d) CIC of 5 EBUS features nomogram for predicting malignant lymph nodes in the validation set. CIC, clinical impact curve; DCA, decision curve analysis.
Figure 6
Figure 6
Developed a 3 EBUS features nomogram with the following features: long-axis, echogenicity, and CHS. CHS, central hilar structure.
Figure 7
Figure 7
The performance of 3 EBUS features nomogram in the training set and the validation set. (a) ROC curve of 3 EBUS features nomogram for predicting the malignancy outcomes of the EBUS-TBNA postoperative negative lymph nodes in the training dataset. (b) ROC curve of 3 EBUS features nomogram for predicting the malignancy outcomes of the EBUS-TBNA postoperative negative lymph nodes in the validation dataset. (c) Calibration curve of 3 EBUS features nomogram for predicting the malignancy outcomes of the EBUS-TBNA postoperative negative lymph nodes in the training dataset. (d) Calibration curve of 3 EBUS features nomogram for predicting the malignancy outcomes of the EBUS-TBNA postoperative negative lymph nodes in the validation dataset.
Figure 8
Figure 8
DCA and CIC of 3 EBUS features nomogram in the training set and the validation set. (a) DCA of 3 EBUS features nomogram for predicting the malignancy outcomes of the EBUS-TBNA postoperative negative lymph nodes in the training set. (b) DCA of 3 EBUS features nomogram for predicting the malignancy outcomes of the EBUS-TBNA postoperative negative lymph nodes in the validation set. (c) CIC of 3 EBUS features nomogram for the malignancy outcomes of the EBUS-TBNA postoperative negative lymph nodes in the training set. (d) CIC of 3 EBUS features nomogram for the malignancy outcomes of the EBUS-TBNA postoperative negative lymph nodes in the validation set. CIC, clinical impact curve; DCA, decision curve analysis.
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