Solitary solid pulmonary nodules: a CT-based deep learning nomogram helps differentiate tuberculosis granulomas from lung adenocarcinomas

Abstract

Objectives

To evaluate the differential diagnostic performance of a computed tomography (CT)-based deep learning nomogram (DLN) in identifying tuberculous granuloma (TBG) and lung adenocarcinoma (LAC) presenting as solitary solid pulmonary nodules (SSPNs).

Methods

Routine CT images of 550 patients with SSPNs were retrospectively obtained from two centers. A convolutional neural network was used to extract deep learning features from all lesions. The training set consisted of data for 218 patients. The least absolute shrinkage and selection operator logistic regression was used to create a deep learning signature (DLS). Clinical factors and CT-based subjective findings were combined in a clinical model. An individualized DLN incorporating DLS, clinical factors, and CT-based subjective findings was constructed to validate the diagnostic ability. The performance of the DLN was assessed by discrimination and calibration using internal (n = 140) and external validation cohorts (n = 192).

Results

DLS, gender, age, and lobulated shape were found to be independent predictors and were used to build the DLN. The combination showed better diagnostic accuracy than any single model evaluated using the net reclassification improvement method (p < 0.05). The areas under the curve in the training, internal validation, and external validation cohorts were 0.889 (95% confidence interval [CI], 0.839–0.927), 0.879 (95% CI, 0.813–0.928), and 0.809 (95% CI, 0.746–0.862), respectively. Decision curve analysis and stratification analysis showed that the DLN has potential generalization ability.

Conclusions

The CT-based DLN can preoperatively distinguish between LAC and TBG in patients presenting with SSPNs.

Key Points

• The deep learning nomogram was developed to preoperatively differentiate TBG from LAC in patients with SSPNs.

• The performance of the deep learning feature was superior to that of the radiomics feature.

• The deep learning nomogram achieved superior performance compared to the deep learning signature, the radiomics signature, or the clinical model alone.

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Abbreviations

AUC:

Area under the curve

CI:

Confidence interval

CNN:

Convolutional neural network

CT:

Computed tomography

DCA:

Decision curve analysis

DL:

Deep learning

DLN:

Deep learning nomogram

DLS:

Deep learning signature

HCR:

Handcrafted radiomics

LAC:

Lung adenocarcinoma

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

SSPNs:

Solitary solid pulmonary nodules

TBG:

Identifying tuberculous granuloma

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Funding

This study received funding from the National Natural Science Foundation of China (81960324, 61967004) and the incubation project of 1000 Young and Middle-Aged Key Teachers in Guangxi Universities (2018GXQGFB160).

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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 declare that they have no conflict of interest.

Statistics and biometry

Bao Feng kindly provided statistical advice for this manuscript.

Bao Feng and YeHang Chen have significant statistical expertise.

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.

Methodology

• Retrospective

• Diagnostic or prognostic study

• Multicenter study

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Feng, B., Chen, X., Chen, Y. et al. Solitary solid pulmonary nodules: a CT-based deep learning nomogram helps differentiate tuberculosis granulomas from lung adenocarcinomas. Eur Radiol (2020). https://doi.org/10.1007/s00330-020-07024-z

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Keywords

  • Tuberculosis
  • Lung adenocarcinoma
  • Solitary pulmonary nodule
  • Deep learning