Skip to main content

Advertisement

SpringerLink
Log in

A predictive nomogram for two-year growth of CT-indeterminate small pulmonary nodules

  • Chest
  • Published:
European Radiology Aims and scope Submit manuscript

Abstract

Objectives

Lung cancer is the most common cancer and the leading cause of cancer-related death worldwide. The optimal management of computed tomography (CT)-indeterminate pulmonary nodules is important. To optimize individualized follow-up strategies, we developed a radiomics nomogram for predicting 2-year growth in case of indeterminate small pulmonary nodules.

Methods

A total of 215 histopathology-confirmed small pulmonary nodules (21 benign and 194 malignant) in 205 patients with ultra-high-resolution CT (U-HRCT) were divided into growth and nongrowth nodules and were randomly allocated to the primary (n = 151) or validation (n = 64) group. The least absolute shrinkage and selection operator (LASSO) method was used for radiomics feature selection and radiomics signature determination. Multivariable logistic regression analysis was used to develop a radiomics nomogram that integrated the radiomics signature with significant clinical parameters (sex and nodule type). The area under the curve (AUC) was applied to assess the predictive performance of the radiomics nomogram. The net benefit of the radiomics nomogram was assessed using a clinical decision curve.

Results

The radiomics signature and nomogram yielded AUCs of 0.892 (95% confidence interval [CI]: 0.843–0.940) and 0.911 (95% CI: 0.867–0.955), respectively, in the primary group and 0.826 (95% CI: 0.727–0.926) and 0.843 (95% CI: 0.749–0.937), respectively, in the validation group. The clinical usefulness of the nomogram was demonstrated by decision curve analysis.

Conclusions

A radiomics nomogram was developed by integrating the radiomics signature with clinical parameters and was easily used for the individualized prediction of two-year growth in case of CT-indeterminate small pulmonary nodules.

Key Points

• A radiomics nomogram was developed for predicting the two-year growth of CT-indeterminate small pulmonary nodules.

• The nomogram integrated a CT-based radiomics signature with clinical parameters and was valuable in developing an individualized follow-up strategy for patients with indeterminate small pulmonary nodules.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

Similar content being viewed by others

Abbreviations

AIC:

Akaike information criterion

CDC:

Clinical decision curve

CI:

Confidence interval

FOV:

Field of view

GGN:

Ground-glass nodule

LASSO:

Least absolute shrinkage and selection operator

U-HRCT:

Ultra-high-resolution computed tomography

VDT:

Volume-doubling time

VOI:

Volume of interest

References

  1. She J, Yang P, Hong QY, Bai CX (2013) Lung cancer in China challenges and interventions. Chest 143:1117–1126

    Article  Google Scholar 

  2. Bray F, Ferlay J, Soerjomataram I, Siegel RL, Torre LA, Jemal A (2018) Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer 68:394–424

    Article  Google Scholar 

  3. Gould MK, Tang T, Liu ILA et al (2015) Recent trends in the identification of incidental pulmonary nodules. Am J Respir Crit Care Med 192:1208–1214

    Article  Google Scholar 

  4. Siegel RL, Miller KD, Jemal A (2020) Cancer statistics, 2020. CA Cancer J Clin 70:7–30

    Article  Google Scholar 

  5. Gopal M, Abdullah SE, Grady JJ, Goodwin JS (2010) Screening for lung cancer with low-dose computed tomography: a systematic review and meta-analysis of the baseline findings of randomized controlled trials. J Thorac Oncol 5:1233–1239

    Article  Google Scholar 

  6. Hawkins S, Wang H, Liu Y et al (2016) Predicting malignant nodules from screening CT scans. J Thorac Oncol 11:2120–2128

    Article  Google Scholar 

  7. Shin KE, Lee KS, Yi CA, Chung MJ, Shin MH, Choi YH (2014) Subcentimeter lung nodules stable for 2 years at LDCT: Long-term follow-up using volumetry. Respirology 19:921–928

    Article  Google Scholar 

  8. Kobayashi Y, Fukui T, Ito S et al (2013) How long should small lung lesions of ground-glass opacity be followed? J Thorac Oncol 8:309–314

    Article  Google Scholar 

  9. Henschke CI, Yankelevitz DF, Mirtcheva R et al (2002) CT screening for lung cancer: frequency and significance of part-solid and nonsolid nodules. AJR Am J Roentgenol 178:1053–1057

    Article  Google Scholar 

  10. Song YS, Park CM, Park SJ, Lee SM, Jeon YK, Goo JM (2014) Volume and mass doubling times of persistent pulmonary subsolid nodules detected in patients without known malignancy. Radiology 273:276–284

    Article  Google Scholar 

  11. Fang WT, Xiang YW, Zhong CX, Chen QH (2014) The IASLC/ATS/ERS classification of lung adenocarcinoma-a surgical point of view. J Thorac Dis 6:S552–S560

    PubMed  PubMed Central  Google Scholar 

  12. Mets OM, de Jong PA, Chung K, Lammers JJ, van Ginneken B, Schaefer-Prokop CM (2016) Fleischner recommendations for the management of subsolid pulmonary nodules: high awareness but limited conformance - a survey study. Eur Radiol 26:3840–3849

    Article  Google Scholar 

  13. Miyata T, Yanagawa M, Hata A et al (2020) Influence of field of view size on image quality: ultra-high-resolution CT vs. conventional high-resolution CT. Eur Radiol 30:3324–3333

    Article  Google Scholar 

  14. Zhu HY, Zhang L, Wang YL et al (2017) Improved image quality and diagnostic potential using ultra-high-resolution computed tomography of the lung with small scan FOV: a prospective study. PLoS One 12:e0172688

    Article  Google Scholar 

  15. Gillies RJ, Kinahan PE, Hricak H (2016) Radiomics: Images are more than pictures, they are data. Radiology 278:563–577

    Article  Google Scholar 

  16. Lambin P, Rios-Velazquez E, Leijenaar R et al (2012) Radiomics: extracting more information from medical images using advanced feature analysis. Eur J Cancer 48:441–446

    Article  Google Scholar 

  17. Liu AL, Wang ZH, Yang YC et al (2020) Preoperative diagnosis of malignant pulmonary nodules in lung cancer screening with a radiomics nomogram. Cancer Commun (Lond) 40:16–24

    Article  CAS  Google Scholar 

  18. Xue X, Yang Y, Huang Q et al (2018) Use of a radiomics model to predict tumor invasiveness of pulmonary adenocarcinomas appearing as pulmonary ground-glass nodules. Biomed Res Int 2018:6803971

    PubMed  PubMed Central  Google Scholar 

  19. Ferreira-Junior JR, Koenigkam-Santos M, MagalhãesTenório AP et al (2020) CT-based radiomics for prediction of histologic subtype and metastatic disease in primary malignant lung neoplasms. Int J Comput Assist Radiol Surg 15:163–172

    Article  Google Scholar 

  20. Digumarthy SR, Padole AM, Rastogi S et al (2019) Predicting malignant potential of subsolid nodules: can radiomics preempt longitudinal follow up CT? Cancer Imaging 19:36

    Article  Google Scholar 

  21. Sun Q, Huang Y, Wang J et al (2019) Applying CT texture analysis to determine the prognostic value of subsolid nodules detected during low-dose CT screening. Clin Radiol 74:59–66

    Article  CAS  Google Scholar 

  22. Nair A, Baldwin DR, Field JK, Hansell DM, Devaraj A (2012) Measurement methods and algorithms for the management of solid nodules. J Thorac Imaging 27:230–239

    Article  Google Scholar 

  23. Scholten ET, de Jong PA, de Hoop B et al (2015) Towards a close computed tomography monitoring approach for screen detected subsolid pulmonary nodules? Eur Resp J 45:765–773

    Article  Google Scholar 

  24. Revel MP, Mannes I, Benzakoun J et al (2018) Subsolid lung nodule classification: a CT criterion for improving interobserver agreement. Radiology 286:316–325

    Article  Google Scholar 

  25. Lindell RM, Hartman TE, Swensen SJ, Jet JR, Midthun DE, Mandrekar JN (2009) 5-Year lung cancer screening experience growth curves of 18 lung cancers compared to histologic type, CT attenuation, stage, survival, and size. Chest 136:1586–1595

    Article  Google Scholar 

  26. Mets OM, Chung K, Zanen P et al (2018) In vivo growth of 60 non-screening detected lung cancers: a computed tomography study. Eur Resp J 51:1702183

    Article  Google Scholar 

  27. de Margerie-Mellon C, Ngo LH, Gill RR et al (2020) The growth rate of subsolid lung adenocarcinoma nodules at chest CT. Radiology 297:189–198

    Article  Google Scholar 

  28. Lee SW, Leem CS, Kim TJ et al (2013) The long-term course of ground-glass opacities detected on thin-section computed tomography. Respir Med 107:904–910

    Article  Google Scholar 

  29. Hiramatsu M, Inagaki T, Inagaki T et al (2008) Pulmonary ground-glass opacity (GGO) lesions-large size and a history of lung cancer are risk factors for growth. J Thorac Oncol 3:1245–1250

    Article  Google Scholar 

  30. Qiu TC, Ru XS, Yin K, Yu J, Song Y, Wu JL (2020) Two nomograms based on CT features to predict tumor invasiveness of pulmonary adenocarcinoma and growth in pure GGN: a retrospective analysis. Jpn J Radiol 38:761–770

    Article  CAS  Google Scholar 

  31. Kakinuma R, Moriyama N, Muramatsu Y et al (2015) Ultra-high-resolution computed tomography of the lung: image quality of a prototype scanner. PLoS One 10:e0137165

    Article  Google Scholar 

  32. Sheshadri A, Rodriguez A, Chen R et al (2015) Effect of reducing field of view on multidetector quantitative computed tomography parameters of airway wall thickness in asthma. J Comput Assist Tomogr 39:584–590

    Article  Google Scholar 

Download references

Funding

This work has received funding from Shanghai Chest Hospital (No. 2019YNJCQ02), Shanghai Municipal Health Commission (No. ZK2019B01), National Natural Science Foundation of China (No. 82071873, 81871353), and Science and Technology Commission of Shanghai Xuhui District Municipality (No. 2020–010).

Author information

Author notes

  1. Limin Xue and Ying Li contributed equally to the work.

Authors and Affiliations

  1. Department of Radiology, Jinshan Hospital, Fudan University, 1508 Longhang Road, Shanghai, 201508, China

    Li Min Xue, Ying Li, Shu Chao Wang & Jin Wei Qiang

  2. Shanghai Institute of Medical Imaging, Shanghai, 200032, China

    Li Min Xue

  3. Department of Radiology, Shanghai Chest Hospital, Shanghai Jiao Tong University, 241 Huaihai Road, Shanghai, 200032, China

    Yu Zhang, Jian Ding Ye & Hong Yu

  4. Department of Radiology, Zhongshan Hospital, Fudan University, 108 Fenglin Road, Shanghai, 200032, China

    Ran Ying Zhang

Authors
  1. Li Min Xue
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ying Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Yu Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Shu Chao Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Ran Ying Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Jian Ding Ye
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Hong Yu
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Jin Wei Qiang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding authors

Correspondence to Hong Yu or Jin Wei Qiang.

Ethics declarations

Guarantor

The scientific guarantor of this publication is Jinwei Qiang.

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

Ying Li has significant statistical expertise.

Informed consent

Written informed consent was obtained from all patients in this study.

Ethical approval

Institutional Review Board approval was obtained by Shanghai Chest Hospital.

Study subjects or cohorts overlap

No study subjects or cohorts overlap.

Methodology

• retrospective

• diagnostic or prognostic study

• performed at one institution

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Below is the link to the electronic supplementary material.

Supplementary file1 (DOCX 6792 KB)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Xue, L.M., Li, Y., Zhang, Y. et al. A predictive nomogram for two-year growth of CT-indeterminate small pulmonary nodules. Eur Radiol 32, 2672–2682 (2022). https://doi.org/10.1007/s00330-021-08343-5

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00330-021-08343-5

Keywords

Search

Navigation