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Two nomograms based on radiomics models using triphasic CT for differentiation of adrenal lipid-poor benign lesions and metastases in a cancer population: an exploratory study

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

Abstract

Objectives

To investigate the effectiveness of CT-based radiomics nomograms in differentiating adrenal lipid-poor benign lesions and metastases in a cancer population.

Methods

This retrospective study enrolled 178 patients with cancer history from three medical centres categorised as those with adrenal lipid-poor benign lesions or metastases. Patients were divided into training, validation, and external testing cohorts. Radiomics features were extracted from triphasic CT images (unenhanced, arterial, and venous) to establish three single-phase models and one triphasic radiomics model using logistic regression. Unenhanced and triphasic nomograms were established by incorporating significant clinico-radiological factors and radscores. The models were evaluated by the receiver operating characteristic curve, Delong’s test, calibration curve, and decision curve.

Results

Lesion side, diameter, and enhancement ratio resulted as independent factors and were selected into nomograms. The areas under the curves (AUCs) of unenhanced and triphasic radiomics models in validation (0.878, 0.914, p = 0.381) and external testing cohorts (0.900, 0.893, p = 0.882) were similar and higher than arterial and venous models (validation: 0.842, 0.765; testing: 0.814, 0.806). Unenhanced and triphasic nomograms yielded similar AUCs in validation (0.903, 0.906, p = 0.955) and testing cohorts (0.928, 0.946, p = 0.528). The calibration curves showed good agreement and decision curves indicated satisfactory clinical benefits.

Conclusion

Unenhanced and triphasic CT-based radiomics nomograms resulted as a useful tool to differentiate adrenal lipid-poor benign lesions from metastases in a cancer population. They exhibited similar predictive efficacies, indicating that enhanced examinations could be avoided in special populations.

Key Points

• All four radiomics models and two nomograms using triphasic CT images exhibited favourable performances in three cohorts to characterise the cancer population’s adrenal benign lesions and metastases.

• Unenhanced and triphasic radiomics models had similar predictive performances, outperforming arterial and venous models.

• Unenhanced and triphasic nomograms also exhibited similar efficacies and good clinical benefits, indicating that contrast-enhanced examinations could be avoided when identifying adrenal benign lesions and metastases.

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Abbreviations

APW:

Absolute percentage wash-out

AUC :

Area under the curve

ER:

Enhancement ratio

FNAB:

Fine needle aspiration biopsy

ICC:

Inter correlation coefficient

LASSO:

Least Absolute Shrinkage and Selection Operator

PWI:

Percentage wash-in

ROC:

Receiver operating characteristic curve

ROI:

Region of interest

RPW:

Relative percentage wash-out

RQS:

Radiomics quality score

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Acknowledgements

We sincerely thank Jingjing Cui and Kui Sun for their assistance with radiomics analysis. We also thank Xinya Zhao for his assistance with the article revision.

Funding

This study was supported by the National Natural Science Foundation of China (81871354, 81571672) and the Academic Promotion Program of Shandong First Medical University (2019QL023).

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Authors and Affiliations

  1. Department of Radiology, Shandong Provincial Hospital, Shandong University, Jinan, 250021, Shandong, China

    Gongzheng Wang, Bing Kang, Congshan Ji & Ximing Wang

  2. United Imaging Intelligence (Beijing) Co., Ltd., Beijing, 100094, China

    Jingjing Cui

  3. Department of Radiology, Qilu Hospital of Shandong University, Jinan, 250012, Shandong, China

    Yan Deng & Yun Zhao

  4. Department of Radiology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, 250021, Shandong, China

    Congshan Ji & Ximing Wang

Authors
  1. Gongzheng Wang
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  4. Yan Deng
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Corresponding authors

Correspondence to Congshan Ji or Ximing Wang.

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Guarantor

The scientific guarantor of this publication is Ximing Wang.

Conflict of interest

Author Jingjing Cui declares relationships with the following companies: United Imaging Intelligence (Beijing) Co., Ltd. The remaining 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

Jingjing Cui (one of the authors) and Kui Sun (not included in the authors) have significant statistical expertise.

Informed consent

The need for informed consent was waived by the Institutional Review Board.

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Institutional Review Board approval was obtained.

Methodology

• retrospective

• diagnostic or prognostic study

• multicenter study

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Wang, G., Kang, B., Cui, J. et al. Two nomograms based on radiomics models using triphasic CT for differentiation of adrenal lipid-poor benign lesions and metastases in a cancer population: an exploratory study. Eur Radiol 33, 1873–1883 (2023). https://doi.org/10.1007/s00330-022-09182-8

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  • DOI: https://doi.org/10.1007/s00330-022-09182-8

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