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. 2023 Mar 9:13:1048205.
doi: 10.3389/fonc.2023.1048205. eCollection 2023.

Clinical features combined with ultrasound-based radiomics nomogram for discrimination between benign and malignant lesions in ultrasound suspected supraclavicular lymphadenectasis

Jieli Luo  1   2 , Peile Jin  1   2 , Jifan Chen  1   2 , Yajun Chen  1   2 , Fuqiang Qiu  1   2 , Tingting Wang  1   2 , Ying Zhang  1   2 , Huili Pan  1   2 , Yurong Hong  1   2 , Pintong Huang  1   2   3
Affiliations

Clinical features combined with ultrasound-based radiomics nomogram for discrimination between benign and malignant lesions in ultrasound suspected supraclavicular lymphadenectasis

Jieli Luo et al. Front Oncol. .

Abstract

Background: Conventional ultrasound (CUS) is the first choice for discrimination benign and malignant lymphadenectasis in supraclavicular lymph nodes (SCLNs), which is important for the further treatment. Radiomics provide more comprehensive and richer information than radiographic images, which are imperceptible to human eyes.

Objective: This study aimed to explore the clinical value of CUS-based radiomics analysis in preoperative differentiation of malignant from benign lymphadenectasis in CUS suspected SCLNs.

Methods: The characteristics of CUS images of 189 SCLNs were retrospectively analyzed, including 139 pathologically confirmed benign SCLNs and 50 malignant SCLNs. The data were randomly divided (7:3) into a training set (n=131) and a validation set (n=58). A total of 744 radiomics features were extracted from CUS images, radiomics score (Rad-score) built were using least absolute shrinkage and selection operator (LASSO) logistic regression. Rad-score model, CUS model, radiomics-CUS (Rad-score + CUS) model, clinic-radiomics (Clin + Rad-score) model, and combined CUS-clinic-radiomics (Clin + CUS + Rad-score) model were built using logistic regression. Diagnostic accuracy was assessed by receiver operating characteristic (ROC) curve analysis.

Results: A total of 20 radiomics features were selected from 744 radiomics features and calculated to construct Rad-score. The AUCs of Rad-score model, CUS model, Clin + Rad-score model, Rad-score + CUS model, and Clin + CUS + Rad-score model were 0.80, 0.72, 0.85, 0.83, 0.86 in the training set and 0.77, 0.80, 0.82, 0.81, 0.85 in the validation set. There was no statistical significance among the AUC of all models in the training and validation set. The calibration curve also indicated the good predictive performance of the proposed nomogram.

Conclusions: The Rad-score model, derived from supraclavicular ultrasound images, showed good predictive effect in differentiating benign from malignant lesions in patients with suspected supraclavicular lymphadenectasis.

Keywords: lymphadenectasis; nomogram; radiomics; supraclavicular lymph node; ultrasound.

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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
The flowchart of patient selection for dividing into the training set and validation set.
Figure 2
Figure 2
The flowchart of CUS segmentation, feature extraction, feature selection and models construction.
Figure 3
Figure 3
Variables extracted from benign and malignant supraclavicular lymph node. (A) The ROI of benign and malignant supraclavicular lymph node. (B) The Mean-square error plot of LASSO regression in supraclavicular lymph node. (C) The density plots between extracted radiomics variables in benign and malignant supraclavicular lymph node.
Figure 4
Figure 4
The CUS-based radiomics nomogram and calibration curves of the nomogram. (A) Integrating Rad-score, shape, and tumor history, the CUS-based nomogram was established. Calibration curves of the nomogram in the training (B) and testing (C) set.
Figure 5
Figure 5
Receiver operating characteristic (ROC) curves of all models in training set (A) and validation set (B).
See this image and copyright information in PMC

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