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European Radiology

, Volume 29, Issue 1, pp 439–449 | Cite as

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

  • TingDan Hu
  • ShengPing Wang
  • Lv Huang
  • JiaZhou Wang
  • DeBing Shi
  • Yuan Li
  • Tong TongEmail author
  • Weijun PengEmail author
Oncology

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.

Keywords

Colorectal neoplasms Nomograms Decision making 

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

Notes

Funding

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

Compliance with ethical standards

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

Supplementary material

330_2018_5539_MOESM1_ESM.docx (5.2 mb)
ESM 1 (DOCX 5286 kb)

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Copyright information

© European Society of Radiology 2018

Authors and Affiliations

  • TingDan Hu
    • 1
  • ShengPing Wang
    • 1
  • Lv Huang
    • 2
  • JiaZhou Wang
    • 2
  • DeBing Shi
    • 3
  • Yuan Li
    • 4
  • Tong Tong
    • 1
    Email author
  • Weijun Peng
    • 1
    Email author
  1. 1.Department of Radiology, Fudan University Shanghai Cancer Center, Department of Oncology, Shanghai Medical CollegeFudan UniversityShanghaiPeople’s Republic of China
  2. 2.Department of RadiotherapyFudan University Shanghai Cancer Center, Fudan UniversityShanghaiPeople’s Republic of China
  3. 3.Department of Colorectal SurgeryFudan University Shanghai Cancer Center, Fudan UniversityShanghaiPeople’s Republic of China
  4. 4.Department of PathologyFudan University Shanghai Cancer Center, Fudan UniversityShanghaiPeople’s Republic of China

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