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Preoperative contrast-enhanced CT-based radiomics nomogram for differentiating benign and malignant primary retroperitoneal tumors

  • Imaging Informatics and Artificial Intelligence
  • Published:
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Abstract

Objectives

This study evaluated the ability of a preoperative contrast-enhanced CT (CECT)–based radiomics nomogram to differentiate benign and malignant primary retroperitoneal tumors (PRT).

Methods

Images and data from 340 patients with pathologically confirmed PRT were randomly placed into training (n = 239) and validation sets (n = 101). Two radiologists independently analyzed all CT images and made measurements. Key characteristics were identified through least absolute shrinkage selection combined with four machine-learning classifiers (support vector machine, generalized linear model, random forest, and artificial neural network back propagation) to create a radiomics signature. Demographic data and CECT characteristics were analyzed to formulate a clinico-radiological model. Independent clinical variables were merged with the best-performing radiomics signature to develop a radiomics nomogram. The discrimination capacity and clinical value of three models were quantified by the area under the receiver operating characteristics (AUC), accuracy, and decision curve analysis.

Results

The radiomics nomogram was able to consistently differentiate between benign and malignant PRT in the training and validation datasets, with AUCs of 0.923 and 0.907, respectively. Decision curve analysis manifested that the nomogram achieved higher clinical net benefits than did separate use of the radiomics signature and clinico-radiological model.

Conclusions

The preoperative nomogram is valuable for differentiating between benign and malignant PRT; it can also aid in treatment planning.

Key Points

• A noninvasive and accurate preoperative determination of benign and malignant PRT is crucial to identifying suitable treatments and predicting disease prognosis.

• Associating the radiomics signature with clinical factors facilitates differentiation of malignant from benign PRT with improved diagnostic efficacy (AUC) and accuracy from 0.772 to 0.907 and from 0.723 to 0.842, respectively, compared with the clinico-radiological model alone.

• For some PRT with anatomically special locations and when biopsy is extremely difficult and risky, a radiomics nomogram may provide a promising preoperative alternative for distinguishing benignity and malignancy.

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Abbreviations

ACC:

Accuracy

ANN-BP:

Artificial neural network back propagation

AUC:

Area under the receiver operating characteristics (ROC) curve

CECT:

Contrast-enhanced computed tomography

CI:

Confidence interval

CT:

Computed tomography

DCA:

Decision curve analysis

GLCM:

Gray-level co-occurrence matrix

GLDM:

Gray-level dependence matrix

GLM:

Generalized linear model

GLRLM:

Gray-level run length matrix

GLSZM:

Gray-level size zone matrix

HU:

Hounsfield units

ICCs:

Intraobserver interobserver correlation coefficients

LASSO:

Least absolute shrinkage and selection operator

NGTDM:

Neighborhood gray-tone difference matrix

PRT:

Primary retroperitoneal tumor

Rad-score:

Radiomics score

ROI:

Region of interest

VOI:

Volume of interest

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Acknowledgements

We thank Ryan Chastain-Gross, Ph.D., from Liwen Bianji (Edanz) (www.liwenbianji.cn/) for editing the English text of a draft of this manuscript.

Funding

This work was supported by the National Natural Science Foundation of China (Grant No. 82172035). This study was funded by Project Grant No. ZR2020MH286 and No. ZR2021MH159 supported by Shandong Provincial Natural Science Foundation. This study was funded by the Clinical Medicine + X Project of the Affiliated Hospital of Qingdao University (Grant No. QDFY + X2021015). This work was supported by the Medicine and Health Technology Development Program of Shandong Province (Grant No. 2019WS373).

Author information

Author notes

  1. Jun Xu and Jia Guo have contributed equally to this work and share first authorship.

Authors and Affiliations

  1. Department of Radiology, The Affiliated Hospital of Qingdao University, Qingdao, Shandong, China

    Jun Xu, Jia Guo, Qing-lian Ji, Hao-yu Liang, Shun-li Liu & He-xiang Wang

  2. Institute for Future Shandong Key Laboratory of Industrial Control Technology of Qingdao University, Qingdao, Shandong, China

    Hai-qiang Yang & Rui-jie Song

  3. Department of Pathology, The Affiliated Hospital of Qingdao University, Qingdao, Shandong, China

    Feng Hou

  4. Department of Hepatopancreatobiliary & Retroperitoneal Tumour Surgery, The Affiliated Hospital of Qingdao University, Qingdao, Shandong, China

    Lan-tian Tian

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  1. Jun Xu
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Corresponding authors

Correspondence to Lan-tian Tian or He-xiang Wang.

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Guarantor

The scientific guarantor of this publication is He-xiang Wang.

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

Haiqiang Yang and Ruijie Song have significant statistical expertise.

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Written informed consent was waived by the Institutional Review Board.

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Institutional Review Board approval was obtained.

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• retrospective

• diagnostic or prognostic study

• performed at one institution

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Xu, J., Guo, J., Yang, Hq. et al. Preoperative contrast-enhanced CT-based radiomics nomogram for differentiating benign and malignant primary retroperitoneal tumors. Eur Radiol 33, 6781–6793 (2023). https://doi.org/10.1007/s00330-023-09686-x

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