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Radiomics analysis of dual-energy CT-derived iodine maps for diagnosing metastatic cervical lymph nodes in patients with papillary thyroid cancer

  • Imaging Informatics and Artificial Intelligence
  • Published:
European Radiology Aims and scope Submit manuscript

Abstract

Objective

To investigate the value of radiomics analysis of dual-energy computed tomography (DECT)–derived iodine maps for preoperative diagnosing cervical lymph nodes (LNs) metastasis in patients with papillary thyroid cancer (PTC).

Methods

Two hundred and fifty-five LNs (143 non-metastatic and 112 metastatic) were enrolled and allocated to training and validation sets (7:3 ratio). Radiomics features were extracted from arterial and venous phase iodine maps, respectively. Radiomics signature was constructed based on reproducible features using the least absolute shrinkage and selection operator (LASSO) logistic regression algorithm with 10-fold cross-validation. Logistic regression modeling was employed to build models based on CT image features (model 1), radiomics signature (model 2), and the combined (model 3). A nomogram was plotted for the combined model and decision curve analysis was applied for clinical use. Diagnostic performance was assessed and compared. Internal validation was performed on an independent set containing 78 LNs.

Results

Model 3 showed optimal diagnostic performance in both training (AUC = 0.933) and validation set (AUC = 0.895), followed by model 2 (training set, AUC = 0.910; validation set, AUC = 0.847). Both these two models outperformed model 1 in both training (AUC = 0.763) (p < 0.05) and validation set (AUC = 0.728) (p < 0.05).

Conclusion

Radiomics analysis of DECT-derived iodine maps showed better diagnostic performance than qualitative evaluation of CT image features in preoperative diagnosing cervical LN metastasis in PTC patients. Radiomics signature integrated with CT image features can serve as a promising imaging biomarker for the differentiation.

Key Points

• Conventional CT image features have limited value for the diagnosis of metastatic LNs in PTC patients.

• Radiomics analysis of dual-energy CT-derived iodine maps significantly outperformed qualitative CT image features in differentiating metastatic from non-metastatic LNs.

• Radiomics signature integrated with qualitative CT image features can serve as a useful tool in judging LNs status, thus aiding clinical decision-making.

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Abbreviations

AIC :

Akaike information criterion

AUC:

Area under the curve

CT:

Computed tomography

CTDIvol:

CT dose index

DECT:

Dual-energy computed tomography

DLP:

Dose-length product

GLCM:

Gray-level co-occurrence matrix

HNSCC:

Head and neck squamous cell carcinoma

ICC:

Intraclass correlation coefficient

LASSO:

Least absolute shrinkage and selection operator

LNs:

Lymph nodes

NPV:

Negative predictive value

PPV:

Positive predictive value

PTC:

Papillary thyroid cancer

Rad scores:

Radiomics scores

ROC:

Receiver operating characteristic

US:

Ultrasonography

VOIs:

Volumes of interest

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Funding

This study has received funding by the Thyroid Research Program of Young and Middle-aged physicians from China Health Promotion Foundation.

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Authors and Affiliations

  1. Department of Radiology, The First Affiliated Hospital of Nanjing Medical University, No. 300, Guangzhou Rd, Gulou District, Nanjing, 210029, People’s Republic of China

    Yan Zhou, Guo-Yi Su, Hao Hu, Xiao-Quan Xu & Fei-Yun Wu

  2. Siemens Healthineers, Shanghai, People’s Republic of China

    Ying-Qian Ge

  3. Department of Thyroid Surgery, The First Affiliated Hospital of Nanjing Medical University, Nanjing, People’s Republic of China

    Yan Si & Mei-Ping Shen

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  1. Yan Zhou
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Corresponding authors

Correspondence to Xiao-Quan Xu or Fei-Yun Wu.

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The scientific guarantor of this publication is Xiao-Quan Xu.

Conflict of interest

One of the authors of this manuscript (Ying-Qian Ge) is an employee of Siemens Healthineers. The remaining authors declare no relationships with any companies whose products or services may be related to the subject matter of the article.

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No complex statistical methods were necessary for this paper.

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Written informed consent was waived by the Institutional Review Board.

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• retrospective

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Zhou, Y., Su, GY., Hu, H. et al. Radiomics analysis of dual-energy CT-derived iodine maps for diagnosing metastatic cervical lymph nodes in patients with papillary thyroid cancer. Eur Radiol 30, 6251–6262 (2020). https://doi.org/10.1007/s00330-020-06866-x

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  • DOI: https://doi.org/10.1007/s00330-020-06866-x

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