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Development and validation of a deep learning signature for predicting lymph node metastasis in lung adenocarcinoma: comparison with radiomics signature and clinical-semantic model

  • Oncology
  • Published:
European Radiology Aims and scope Submit manuscript

Abstract

Objective

To develop and validate a deep learning (DL) signature for predicting lymph node (LN) metastasis in patients with lung adenocarcinoma.

Methods

A total of 612 patients with pathologically-confirmed lung adenocarcinoma were retrospectively enrolled and were randomly divided into training cohort (n = 489) and internal validation cohort (n = 123). Besides, 108 patients were enrolled and constituted an independent test cohort (n = 108). Patients’ clinical characteristics and CT semantic features were collected. The radiomics features were derived from contrast-enhanced CT images. The clinical-semantic model and radiomics signature were built to predict LN metastasis. Furthermore, Swin Transformer was adopted to develop a DL signature predictive of LN metastasis. Model performance was evaluated by area under the receiver operating characteristic curve (AUC), sensitivity, specificity, calibration curve, and decision curve analysis. The comparisons of AUC were conducted by the DeLong test.

Results

The proposed DL signature yielded an AUC of 0.948–0.961 across all three cohorts, significantly superior to both clinical-semantic model and radiomics signature (all p < 0.05). The calibration curves show that DL signature predicted probabilities fit well the actual observed probabilities of LN metastasis. DL signature gained a higher net benefit than both clinical-semantic model and radiomics signature. The incorporation of radiomics signature or clinical-semantic risk predictors failed to reveal an incremental value over the DL signature.

Conclusions

The proposed DL signature based on Swin Transformer achieved a promising performance in predicting LN metastasis and could confer important information in noninvasive mediastinal LN staging and individualized therapeutic options.

Key Points

• Accurate prediction for lymph node metastasis is crucial to formulate individualized therapeutic options for patients with lung adenocarcinoma.

• The deep learning signature yielded an AUC of 0.948–0.961 across all three cohorts in predicting lymph node metastasis, superior to both radiomics signature and clinical-semantic model.

• The incorporation of radiomics signature or clinical-semantic risk predictors into deep learning signature failed to reveal an incremental value over deep learning signature.

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Abbreviations

AUC:

Area under the curve

CEA:

Carcinoembryonic antigen

CI:

Confidence interval

CS:

Clinical-semantic

CTR:

Consolidation-to-tumor ratio

DL:

Deep learning

ICC:

Intraclass correlation coefficient

LI:

Labeling index

LN:

lymph node

NSCLC:

Non-small cell lung cancer

OR:

Odds ratio

ROC:

Receiver operating characteristic curve

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Funding

This study has received funding from the National Natural Science Foundation of China (NO.81873889).

Author information

Authors and Affiliations

  1. Medical Imaging Center, People’s Hospital of Ningxia Hui Autonomous Region, Yinchuan, China

    Xiaoling Ma

  2. Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, 1095 Jiefang Road, Qiaokou District, Wuhan, 430030, Hubei, China

    Liming Xia, Weijia Wan & Wen Zhou

  3. GE Healthcare, Wuhan, China

    Jun Chen

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  1. Xiaoling Ma
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Corresponding author

Correspondence to Liming Xia.

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Guarantor

The scientific guarantor of this publication is Liming Xia.

Conflict of interest

The authors of this manuscript declare no relationships with any companies whose products or services may be related to the subject matter of the article.

Statistics and biometry

No complex statistical methods were necessary for this paper.

Informed consent

Written informed consent was waived by the Institutional Review Board.

Ethical approval

Institutional Review Board approval was obtained.

Study subjects or cohorts overlap

Some study subjects or cohorts have been previously reported in a prior study, where 182 patients with early-stage lung adenocarcinoma were previously reported on the relationship between CT morphological features and Ki-67 (PMID: 34164176). The present study has a much larger sample size and focuses on predicting lymph node metastasis in lung adenocarcinoma.

Methodology

• retrospective

• diagnostic study

• performed at one institution

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Ma, X., Xia, L., Chen, J. et al. Development and validation of a deep learning signature for predicting lymph node metastasis in lung adenocarcinoma: comparison with radiomics signature and clinical-semantic model. Eur Radiol 33, 1949–1962 (2023). https://doi.org/10.1007/s00330-022-09153-z

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