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Development and validation of a deep learning radiomics nomogram for preoperatively differentiating thymic epithelial tumor histologic subtypes

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

Abstract

Objectives

Using contrast-enhanced computed tomography (CECT) and deep learning technology to develop a deep learning radiomics nomogram (DLRN) to preoperative predict risk status of patients with thymic epithelial tumors (TETs).

Methods

Between October 2008 and May 2020, 257 consecutive patients with surgically and pathologically confirmed TETs were enrolled from three medical centers. We extracted deep learning features from all lesions using a transformer-based convolutional neural network and created a deep learning signature (DLS) using selector operator regression and least absolute shrinkage. The predictive capability of a DLRN incorporating clinical characteristics, subjective CT findings and DLS was evaluated by the area under the curve (AUC) of a receiver operating characteristic curve.

Results

To construct a DLS, 25 deep learning features with non-zero coefficients were selected from 116 low-risk TETs (subtypes A, AB, and B1) and 141 high-risk TETs (subtypes B2, B3, and C). The combination of subjective CT features such as infiltration and DLS demonstrated the best performance in differentiating TETs risk status. The AUCs in the training, internal validation, external validation 1 and 2 cohorts were 0.959 (95% confidence interval [CI]: 0.924–0.993), 0.868 (95% CI: 0.765–0.970), 0.846 (95% CI: 0.750–0.942), and 0.846 (95% CI: 0.735–0.957), respectively. The DeLong test and decision in curve analysis revealed that the DLRN was the most predictive and clinically useful model.

Conclusions

The DLRN comprised of CECT-derived DLS and subjective CT findings showed a high performance in predicting risk status of patients with TETs.

Clinical relevance statement

Accurate risk status assessment of thymic epithelial tumors (TETs) may aid in determining whether preoperative neoadjuvant treatment is necessary. A deep learning radiomics nomogram incorporating enhancement CT-based deep learning features, clinical characteristics, and subjective CT findings has the potential to predict the histologic subtypes of TETs, which can facilitate decision-making and personalized therapy in clinical practice.

Key Points

• A non-invasive diagnostic method that can predict the pathological risk status may be useful for pretreatment stratification and prognostic evaluation in TET patients.

• DLRN demonstrated superior performance in differentiating the risk status of TETs when compared to the deep learning signature, radiomics signature, or clinical model.

• The DeLong test and decision in curve analysis revealed that the DLRN was the most predictive and clinically useful in differentiating the risk status of TETs.

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Abbreviations

AUC:

Area under the curve

CECT:

Contrast-enhanced computed tomography

CI:

Confidence interval

CNN:

Convolutional neural network

CT:

Computed tomography

DCA:

Decision curve analysis

DL:

Deep learning

DLRN:

Deep learning radiomics nomogram

DLS:

Deep learning signature

HE:

Hematoxylin-eosin

IDI:

Integrated discrimination improvement

LASSO:

Least absolute shrinkage and selection operator

NPV:

Negative probability value

NRI:

Net reclassification index

OR:

Odds ratio

PPV:

Positive probability value

ROC:

Receiver operating characteristic

ROI:

Region of interest

RS:

Radiomics signature

TETs:

Thymic epithelial tumors

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Funding

This study has received funding from the National Natural Science Foundation of China (Grant Number: 62176104 and 81960324), the Natural Science Foundation of Guangxi Province (Grant Number: 2021GXNSFAA075037), and the Medical Research Foundation of Guangdong Province (Grant Number: A2021138).

Author information

Author notes

  1. Xiangmeng Chen and Bao Feng contributed equally to this work.

Authors and Affiliations

  1. Department of Radiology, Jiangmen Central Hospital, Jiangmen, Guangdong Province, 529030, People’s Republic of China

    Xiangmeng Chen, Bao Feng, Zhifa Jin & Wansheng Long

  2. Laboratory of Artificial Intelligence of Biomedicine, Guilin University of Aerospace Technology, Guilin, Guangxi Province, 541004, People’s Republic of China

    Bao Feng, Kuncai Xu & Yehang Chen

  3. Department of Nuclear Medicine, Jiangmen Central Hospital, Jiangmen, Guangdong Province, 529030, People’s Republic of China

    Xiaobei Duan

  4. Department of Radiology, The Fifth Affiliated Hospital of Sun Yat-Sen University, Zhuhai, Guangdong Province, 519000, People’s Republic of China

    Kunwei Li

  5. Department of Pathology, Jiangmen Central Hospital, Jiangmen, Guangdong Province, 529030, People’s Republic of China

    Ronggang Li

  6. Department of Radiology, The Seventh Affiliated Hospital of Sun Yat-Sen University, Shenzhen, Guangdong Province, 518107, People’s Republic of China

    Xueguo Liu

Authors
  1. Xiangmeng Chen
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Corresponding authors

Correspondence to Wansheng Long or Xueguo Liu.

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Guarantor

The scientific guarantor of this publication is Wansheng Long.

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

Bao Feng kindly provided statistical advice for this manuscript.

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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Institutional review board approval was obtained.

Study subjects or cohorts overlap

Study subjects or cohorts have not been previously reported.

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• Retrospective

• Diagnostic or prognostic study

• Multicenter study

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Chen, X., Feng, B., Xu, K. et al. Development and validation of a deep learning radiomics nomogram for preoperatively differentiating thymic epithelial tumor histologic subtypes. Eur Radiol 33, 6804–6816 (2023). https://doi.org/10.1007/s00330-023-09690-1

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