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CT-based radiomics and machine learning to predict spread through air space in lung adenocarcinoma

Abstract

Purpose

Spread through air space (STAS) is a novel invasive pattern of lung adenocarcinoma and is also a risk factor for recurrence and worse prognosis of lung adenocarcinoma. The aims of this study are to develop and validate a computed tomography (CT)‑based radiomics model for preoperative prediction of STAS in lung adenocarcinoma.

Methods and materials

This retrospective study was approved by an institutional review board and included 462 (mean age, 58.06 years) patients with pathologically confirmed lung adenocarcinoma. STAS was identified in 90 patients (19.5%). Two experienced radiologists segmented and extracted radiomics features on preoperative thin-slice CT images with radiomics extension independently. Intraclass correlation coefficients (ICC) and Pearson’s correlation were used to rule out those low reliable (ICC < 0.75) and redundant (r > 0.9) features. Univariate logistic regression was applied to select radiomics features which were associated with STAS. A radiomics-based machine learning predictive model using a random forest (RF) was developed and calibrated with fivefold cross-validation. The diagnostic performance of the model was measured by the area under the curve (AUC) of receiver operating characteristic (ROC).

Results

With univariate analysis, 12 radiomics features and age were found to be associated with STAS significantly. The RF model achieved an AUC of 0.754 (a sensitivity of 0.880 and a specificity of 0.588) for predicting STAS.

Conclusion

CT-based radiomics model can preoperatively predict STAS in lung adenocarcinoma with good diagnosis performance.

Key Points

• CT-based radiomics and machine learning model can predict spread through air space (STAS) in lung adenocarcinoma with high accuracy.

• The random forest (RF) model achieved an AUC of 0.754 (a sensitivity of 0.880 and a specificity of 0.588) for predicting STAS.

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Abbreviations

3D-VOIs:

Three-dimensional volumes of interest

AUC:

Area under curve

CT:

Computed tomography

DICOM:

Digital Imaging and Communications in Medicine

GGN:

Ground-glass nodules

GLCM:

Gray level co-occurrence matrix

GLDM:

Gray level dependence matrix

GLRLM:

Gray level run length matrix

GLSZM:

Gray Level size zone matrix

HGLE:

High gray level emphasis

ICC:

Intraclass correlation coefficients

KV:

Kilovolt

LDCT:

Low-dose computed tomography.

MRI:

Magnetic resonance imaging

NGTDM:

Neighboring gray tone difference matrix

PACS:

Picture archiving and communication system

PC:

Personal computer

RF:

Random forest

ROC:

Receiver operating characteristic

STAS:

Spread through air space

References

  1. 1.

    Siegel RL, Miller KD, Jemal A (2019) Cancer statistics, 2019. CA Cancer J Clin 69:7–34

    PubMed  PubMed Central  Google Scholar 

  2. 2.

    Amin MB, Tamboli P, Merchant SH et al (2002) Micropapillary component in lung adenocarcinoma: a distinctive histologic feature with possible prognostic significance. Am J Surg Pathol 26:358–364

    Article  Google Scholar 

  3. 3.

    Blaauwgeers H, Flieder D, Warth A et al (2017) A prospective study of loose tissue fragments in non-small cell lung cancer resection specimens: an alternative view to spread through air spaces. Am J Surg Pathol 41:1226–1230

    Article  Google Scholar 

  4. 4.

    Travis WD, Brambilla E, Nicholson AG et al (2015) The 2015 World Health Organization classification of lung tumors: impact of genetic, clinical and radiologic advances since the 2004 classification. J Thorac Oncol 10:1243–1260

    Article  Google Scholar 

  5. 5.

    Kadota K, Nitadori J, Sima CS et al (2015) Tumor spread through air spaces is an important pattern of invasion and impacts the frequency and location of recurrences after limited resection for small stage I lung adenocarcinomas. J Thorac Oncol 10:806–814

    CAS  Article  Google Scholar 

  6. 6.

    Onozato ML, Kovach AE, Yeap BY et al (2013) Tumor islands in resected early-stage lung adenocarcinomas are associated with unique clinicopathologic and molecular characteristics and worse prognosis. Am J Surg Pathol 37:287–294

    Article  Google Scholar 

  7. 7.

    Shiono S, Yanagawa N (2016) Spread through air spaces is a predictive factor of recurrence and a prognostic factor in stage I lung adenocarcinoma. Interact Cardiovasc Thorac Surg 23:567–572

    Article  Google Scholar 

  8. 8.

    Dai C, Xie H, Su H et al (2017) Tumor spread through air spaces affects the recurrence and overall survival in patients with lung adenocarcinoma >2 to 3 cm. J Thorac Oncol 12:1052–1060

    Article  Google Scholar 

  9. 9.

    de Margerie-Mellon C, Onken A, Heidinger BH, VanderLaan PA, Bankier AA (2018) CT manifestations of tumor spread through airspaces in pulmonary adenocarcinomas presenting as subsolid nodules. J Thorac Imaging 33:402–408

  10. 10.

    Kim SK, Kim TJ, Chung MJ et al (2018) Lung adenocarcinoma: CT features associated with spread through air spaces. Radiology 289:831–840

    Article  Google Scholar 

  11. 11.

    Kumar V, Gu Y, Basu S et al (2012) Radiomics: the process and the challenges. Magn Reson Imaging 30:1234–1248

    Article  Google Scholar 

  12. 12.

    Lambin P, Leijenaar RTH, Deist TM et al (2017) Radiomics: the bridge between medical imaging and personalized medicine. Nat Rev Clin Oncol 14:749–762

    Article  Google Scholar 

  13. 13.

    Bonekamp D, Kohl S, Wiesenfarth M et al (2018) Radiomic machine learning for characterization of prostate lesions with MRI: comparison to ADC values. Radiology 289:128–137

    Article  Google Scholar 

  14. 14.

    Ueno Y, Forghani B, Forghani R et al (2017) Endometrial carcinoma: MR imaging-based texture model for preoperative risk stratification-a preliminary analysis. Radiology 284:748–757

    Article  Google Scholar 

  15. 15.

    Mao L, Chen H, Liang M et al (2019) Quantitative radiomic model for predicting malignancy of small solid pulmonary nodules detected by low-dose CT screening. Quant Imaging Med Surg 9:263–272

    Article  Google Scholar 

  16. 16.

    Mei D, Luo Y, Wang Y, Gong J (2018) CT texture analysis of lung adenocarcinoma: can radiomic features be surrogate biomarkers for EGFR mutation statuses. Cancer Imaging. https://doi.org/10.1186/s40644-018-0184-2

  17. 17.

    Kuo MD, Jamshidi N (2014) Behind the numbers: decoding molecular phenotypes with radiogenomics--guiding principles and technical considerations. Radiology 270:320–325

    Article  Google Scholar 

  18. 18.

    Rutman AM, Kuo MD (2009) Radiogenomics: creating a link between molecular diagnostics and diagnostic imaging. Eur J Radiol 70:232–241

    Article  Google Scholar 

Download references

Acknowledgments

Thanks to all authors for their contribution to this article.

Funding

The authors state that this work has not received any funding.

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Correspondence to Jingshan Gong.

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Guarantor

The scientific guarantor of this publication is Jingshan Gong.

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

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

• Observational

• Performed at one institution

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Jiang, C., Luo, Y., Yuan, J. et al. CT-based radiomics and machine learning to predict spread through air space in lung adenocarcinoma. Eur Radiol 30, 4050–4057 (2020). https://doi.org/10.1007/s00330-020-06694-z

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Keywords

  • Lung
  • Adenocarcinoma
  • Metastasis
  • Radiomics
  • Machine learning