Integrative nomogram of CT imaging, clinical, and hematological features for survival prediction of patients with locally advanced non-small cell lung cancer

  • Linlin Wang
  • Taotao Dong
  • Bowen Xin
  • Chongrui Xu
  • Meiying Guo
  • Huaqi Zhang
  • Dagan Feng
  • Xiuying Wang
  • Jinming Yu



To determine the integrative value of clinical, hematological, and computed tomography (CT) radiomic features in survival prediction for locally advanced non-small cell lung cancer (LA-NSCLC) patients.


Radiomic features and clinical and hematological features of 118 LA-NSCLC cases were firstly extracted and analyzed in this study. Then, stable and prognostic radiomic features were automatically selected using the consensus clustering method with either Cox proportional hazard (CPH) model or random survival forest (RSF) analysis. Predictive radiomic, clinical, and hematological parameters were subsequently fitted into a final prognostic model using both the CPH model and the RSF model. A multimodality nomogram was then established from the fitting model and was cross-validated. Finally, calibration curves were generated with the predicted versus actual survival status.


Radiomic features selected by clustering combined with CPH were found to be more predictive, with a C-index of 0.699 in comparison to 0.648 by clustering combined with RSF. Based on multivariate CPH model, our integrative nomogram achieved a C-index of 0.792 and retained 0.743 in the cross-validation analysis, outperforming radiomic, clinical, or hematological model alone. The calibration curve showed agreement between predicted and actual values for the 1-year and 2-year survival prediction. Interestingly, the selected important radiomic features were significantly correlated with levels of platelet, platelet/lymphocyte ratio (PLR), and lymphocyte/monocyte ratio (LMR) (p values all < 0.05).


The integrative nomogram incorporated CT radiomic, clinical, and hematological features improved survival prediction in LA-NSCLC patients, which would offer a feasible and practical reference for individualized management of these patients.

Key Points

• An integrative nomogram incorporated CT radiomic, clinical, and hematological features was constructed and cross-validated to predict prognosis of LA-NSCLC patients.

• The integrative nomogram outperformed radiomic, clinical, or hematological model alone.

• This nomogram has value to permit non-invasive, comprehensive, and dynamical evaluation of the phenotypes of LA-NSCLC and can provide a feasible and practical reference for individualized management of LA-NSCLC patients.


Non-small cell lung cancer Radiomics Nomogram Prognosis 



Concurrent chemotherapy and radiotherapy


Concordance index


Cox proportional hazard


Computed tomography


Gray-level co-occurrence matrix


Gray-level size zone matrix


Gross tumor volume


Locally advanced non-small cell lung cancer


Lymphocyte/monocyte ratio


Neutrophil/lymphocyte ratio


Platelet/lymphocyte ratio


Response Evaluation Criteria in Solid Tumors


Random survival forest



This work was supported by China Scholarship Fund, the Project of Postdoctoral Science Foundation of China (Grant Nos. 2016M590640 and 2016M592199), the Project of Postdoctoral Innovation of Shandong Province (Grant No. 201501010), National Health and Family Planning Commission of China (201402011), and National Natural Science Foundation of China (81472812).

Compliance with ethical standards


The scientific guarantor of this publication is Jinming Yu.

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.


• prospective

• diagnostic or prognostic study

• performed at one institution


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Copyright information

© European Society of Radiology 2019

Authors and Affiliations

  • Linlin Wang
    • 1
  • Taotao Dong
    • 2
  • Bowen Xin
    • 3
  • Chongrui Xu
    • 3
  • Meiying Guo
    • 1
    • 4
  • Huaqi Zhang
    • 1
    • 5
  • Dagan Feng
    • 3
  • Xiuying Wang
    • 3
  • Jinming Yu
    • 1
  1. 1.Department of Radiation Oncology, Shandong Cancer Hospital Affiliated to Shandong UniversityShandong Academy of Medical ScienceJinanChina
  2. 2.Department of Gynecology and ObstetricsQilu Hospital of Shandong UniversityJinanChina
  3. 3.School of Information Technologiesthe University of SydneySydneyAustralia
  4. 4.Medical College of Shandong UniversityJinanChina
  5. 5.Tianjin Medical UniversityTianjinChina

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