Skip to main content

Advertisement

SpringerLink
Log in

A clinical-radiomics nomogram for the preoperative prediction of lung metastasis in colorectal cancer patients with indeterminate pulmonary nodules

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

Abstract

Objectives

To develop and validate a clinical-radiomics nomogram for preoperative prediction of lung metastasis for colorectal cancer (CRC) patients with indeterminate pulmonary nodules (IPN).

Methods

194 CRC patients with lung nodules were enrolled in this study (136 in the training cohort and 58 in the validation cohort). To evaluate the probability of lung metastasis, we developed three models, the clinical model with significant clinical risk factors, the radiomics model with radiomics features constructed by the least absolute shrinkage and selection operator algorithm, and the clinical-radiomics model with significant variables selected by the stepwise logistic regression. The Akaike information criterion (AIC) was used to compare the relative strength of different models, and the area under the curve (AUC) was used to quantify the predictive accuracy. The nomogram was developed based on the most appropriate model. Decision-curve analysis was applied to assess the clinical usefulness.

Results

The clinical-radiomics model (AIC = 98.893) with the lowest AIC value compared with that of the clinical-only model (AIC = 138.502) or the radiomics-only model (AIC = 116.146) was identified as the best model. The clinical-radiomics nomogram was also successfully developed with favourable discrimination in both training cohort (AUC = 0.929, 95% CI: 0.885–0.974) and validation cohort (AUC = 0.922, 95% CI: 0.857–0.986), and good calibration. Decision-curve analysis confirmed the clinical utility of the clinical-radiomics nomogram.

Conclusions

In CRC patients with IPNs, the clinical-radiomics nomogram created by the radiomics signature and clinical risk factors exhibited favourable discriminatory ability and accuracy for a metastasis prediction.

Key Points

• Clinical features can predict lung metastasis of colorectal cancer patients.

• Radiomics analysis outperformed clinical features in assessing the risk of pulmonary metastasis.

• A clinical-radiomics nomogram can help clinicians predict lung metastasis in colorectal cancer patients.

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

Similar content being viewed by others

Abbreviations

AIC:

Akaike information criterion

AUC:

Area under the curve

CA19-9:

Carbohydrate antigen 19-9

CEA:

Carcinoembryonic antigen

CI:

Confidence interval

CRC:

Colorectal cancer

DCA:

Decision curve analysis

IPN:

Indeterminate pulmonary nodules

ITT:

Intravascular tumour thrombus

LASSO:

Least absolute shrinkage and selection operator

LM:

Lung metastasis

LR test:

Likelihood-ratio test

NM:

Non-metastasis

NPV:

Negative predictive value

PNI:

Perineural invasion

PPV:

Positive predictive value

ROC:

Receiver operating characteristic curve

References

  1. Torre LA, Bray F, Siegel RL, Ferlay J, Lortet-Tieulent J, Jemal A (2015) Global cancer statistics, 2012. CA Cancer J Clin 65:87–108

    Article  Google Scholar 

  2. Kobayashi H, Mochizuki H, Sugihara K et al (2007) Characteristics of recurrence and surveillance tools after curative resection for colorectal cancer: a multicenter study. Surgery 141:67–75

    Article  Google Scholar 

  3. Desch CE, Benson AB 3rd, Somerfield MR et al (2005) Colorectal cancer surveillance: 2005 update of an American Society of Clinical Oncology practice guideline. J Clin Oncol 23:8512–8519

    Article  Google Scholar 

  4. Vachani A, Tanner NT, Aggarwal J et al (2014) Factors that influence physician decision making for indeterminate pulmonary nodules. Ann Am Thorac Soc 11:1586–1591

    Article  Google Scholar 

  5. NM A (2003) The solitary pulmonary nodule. N Engl J Med 349(16):1575

    Google Scholar 

  6. Inoue M, Ohta M, Iuchi K et al (2004) Benefits of surgery for patients with pulmonary metastases from colorectal carcinoma. Ann Thorac Surg 78:238–244

    Article  Google Scholar 

  7. De Wever W, Meylaerts L, De Ceuninck L, Stroobants S, Verschakelen JA (2007) Additional value of integrated PET-CT in the detection and characterization of lung metastases: correlation with CT alone and PET alone. Eur Radiol 17:467–473

    Article  Google Scholar 

  8. Aerts HJ, Velazquez ER, Leijenaar RT et al (2014) Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach. Nat Commun 5:4006

    Article  CAS  Google Scholar 

  9. Zhang Y, Moore GR, Laule C et al (2013) Pathological correlates of magnetic resonance imaging texture heterogeneity in multiple sclerosis. Ann Neurol 74:91–99

    Article  CAS  Google Scholar 

  10. Yoon HJ, Kim Y, Kim BS (2015) Intratumoral metabolic heterogeneity predicts invasive components in breast ductal carcinoma in situ. Eur Radiol 25:3648–3658

    Article  Google Scholar 

  11. Win T, Miles KA, Janes SM et al (2013) Tumor heterogeneity and permeability as measured on the CT component of PET/CT predict survival in patients with non-small cell lung cancer. Clin Cancer Res 19:3591–3599

    Article  CAS  Google Scholar 

  12. Ganeshan B, Goh V, Mandeville HC (2013) Non-small cell lung cancer: histopathologic correlates for texture parameters at CT. Radiology 266:326–336

    Article  Google Scholar 

  13. Ahmed A, Gibbs P, Pickles M, Turnbull L (2013) Texture analysis in assessment and prediction of chemotherapy response in breast cancer. J Magn Reson Imaging 38:89–101

    Article  Google Scholar 

  14. Baek SJ, Kim SH, Kwak JM et al (2012) Indeterminate pulmonary nodules in rectal cancer: a recommendation for follow-up guidelines. J Surg Oncol 106:481–485

    Article  Google Scholar 

  15. Kim CH, Huh JW, Kim HR, Kim YJ (2015) Indeterminate pulmonary nodules in colorectal cancer: follow-up guidelines based on a risk predictive model. Ann Surg 261:1145–1152

    Article  Google Scholar 

  16. Mitry E, Guiu B, Cosconea S, Jooste V, Faivre J, Bouvier AM (2010) Epidemiology, management and prognosis of colorectal cancer with lung metastases: a 30-year population-based study. Gut 59:1383–1388

    Article  Google Scholar 

  17. Cufer T, Ovcaricek T, O’Brien ME (2013) Systemic therapy of advanced non-small cell lung cancer: major-developments of the last 5-years. Eur J Cancer 49:1216–1225

    Article  CAS  Google Scholar 

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

    Article  Google Scholar 

  19. Wei K, Su H, Zhou G et al (2016) Potential Application of Radiomics for Differentiating Solitary Pulmonary Nodules. OMICS J Radiol 5:1-5

  20. Park J, Kobayashi Y, Urayama KY, Yamaura H, Yatabe Y, Hida T (2016) Imaging Characteristics of Driver Mutations in EGFR, KRAS, and ALK among Treatment-Naive Patients with Advanced Lung Adenocarcinoma. PLoS One 11:e0161081

    Article  Google Scholar 

  21. Divine MR, Katiyar P, Kohlhofer U, Quintanilla-Martinez L, Pichler BJ, Disselhorst JA (2016) A Population-Based Gaussian Mixture Model Incorporating 18F-FDG PET and Diffusion-Weighted MRI Quantifies Tumor Tissue Classes. J Nucl Med 57:473–479

    Article  CAS  Google Scholar 

  22. Wu W, Parmar C, Grossmann P et al (2016) Exploratory study to identify radiomics classifiers for lung cancer histology. Front Oncol 6:71

  23. Kamiya A, Murayama S, Kamiya H, Yamashiro T, Oshiro Y, Tanaka N (2014) Kurtosis and skewness assessments of solid lung nodule density histograms: differentiating malignant from benign nodules on CT. Jpn J Radiol 32:14–21

    Article  Google Scholar 

  24. Ko JP, Suh J, Ibidapo O et al (2016) Lung adenocarcinoma: correlation of quantitative CT findings with pathologic findings. Radiology 280:931–939

  25. Son JY, Lee HY, Kim JH et al (2016) Quantitative CT analysis of pulmonary ground-glass opacity nodules for distinguishing invasive adenocarcinoma from non-invasive or minimally invasive adenocarcinoma: the added value of using iodine mapping. Eur Radiol 26:43–54

    Article  Google Scholar 

  26. Hanania AN, Bantis LE, Feng Z et al (2016) Quantitative imaging to evaluate malignant potential of IPMNs. Oncotarget 7:85776–85784

  27. Lee G, Lee HY, Park H et al (2017) Radiomics and its emerging role in lung cancer research, imaging biomarkers and clinical management: State of the art. Eur J Radiol 86:297–307

    Article  Google Scholar 

Download references

Funding

This study has received funding by the National Science Foundation for Young Scientists of China (Grant No.81501437).

Author information

Author notes

  1. TingDan Hu and ShengPing Wang contributed equally to this work.

Authors and Affiliations

  1. Department of Radiology, Fudan University Shanghai Cancer Center, Department of Oncology, Shanghai Medical College, Fudan University, No. 270, Dongan Rd, Shanghai, 200032, People’s Republic of China

    TingDan Hu, ShengPing Wang, Tong Tong & Weijun Peng

  2. Department of Radiotherapy, Fudan University Shanghai Cancer Center, Fudan University, Shanghai, People’s Republic of China

    Lv Huang & JiaZhou Wang

  3. Department of Colorectal Surgery, Fudan University Shanghai Cancer Center, Fudan University, Shanghai, People’s Republic of China

    DeBing Shi

  4. Department of Pathology, Fudan University Shanghai Cancer Center, Fudan University, Shanghai, People’s Republic of China

    Yuan Li

Authors
  1. TingDan Hu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. ShengPing Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Lv Huang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. JiaZhou Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. DeBing Shi
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Yuan Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Tong Tong
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Weijun Peng
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding authors

Correspondence to Tong Tong or Weijun Peng.

Ethics declarations

Guarantor

The scientific guarantor of this publication is Tong Tong.

Conflict of interest

The authors of this article declare no relationships with any companies whose products or services may be related to the subject matter of the article.

Statistics and biometry

Shengping Wang has significant statistical expertise.

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

Electronic supplementary material

ESM 1

(DOCX 5286 kb)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Hu, T., Wang, S., Huang, L. et al. A clinical-radiomics nomogram for the preoperative prediction of lung metastasis in colorectal cancer patients with indeterminate pulmonary nodules. Eur Radiol 29, 439–449 (2019). https://doi.org/10.1007/s00330-018-5539-3

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00330-018-5539-3

Keywords

Search

Navigation