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A radiomics nomogram based on multiparametric MRI might stratify glioblastoma patients according to survival

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Abstract

Objectives

To construct a radiomics nomogram for the individualized estimation of the survival stratification in glioblastoma (GBM) patients using the multiregional information extracted from multiparametric MRI, which could facilitate the clinical decision-making for GBM patients.

Materials and methods

A total of 105 eligible GBM patients (57 in the long-term and 48 in the short-term survival groups, separated by an overall survival of 12 months) were selected from the Cancer Genome Atlas. These patients were divided into a training set (n = 70) and a validation set (n = 35). Radiomics features (n = 4000) were extracted from multiple regions of the GBM using multiparametric MRI. Then, a radiomics signature was constructed using least absolute shrinkage and selection operator regression for each patient in the training set. Combined with clinical risk factors, a radiomics nomogram was constructed based on a multivariate logistic regression model. The performance of this radiomics nomogram was assessed by calibration, discrimination, and clinical usefulness.

Results

The radiomics signature consisted of 25 selected features and performed better than clinical risk factors (i.e., age, Karnofsky performance status, and treatment strategy) in survival stratification. When the radiomics signature and clinical risk factors were combined, the radiomics nomogram exhibited promising discrimination in the training (C-index, 0.971) and validation (C-index, 0.974) sets. The favorable calibration and decision curve analysis indicated the clinical usefulness of the radiomics nomogram.

Conclusions

The presented radiomics nomogram, as a non-invasive prediction tool, could exhibit a favorable predictive accuracy and provide individualized probabilities of survival stratification for GBM patients.

Key Points

• Non-invasive survival stratification of GBM patients can be obtained with a radiomics nomogram.

• The proposed nomogram constructed by radiomics signature selected from 4000 radiomics features, combined with independent clinical risk factors such as age, Karnofsky performance status, and treatment strategy.

• The proposed radiomics nomogram exhibited good calibration and discrimination for survival stratification of GBM patients in both training (C-index, 0.971) and validation (C-index, 0.974) sets.

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Abbreviations

2D:

Two-dimensional

3D:

Three-dimensional

AUC:

Area under the curve

DCA:

Decision curve analysis

FLAIR:

Fluid-attenuated inversion recovery

GBM:

Glioblastoma

IDH:

Isocitrate dehydrogenase

KPS:

Karnofsky performance status

LASSO:

Least absolute shrinkage and selection operator

MGMT:

Methylated O6-methylguanine-DNA methyltransferase

OS:

Overall survival

rCET:

The region of contrast-enhanced tumor

rE/nCET:

The region of edema/non-contrast-enhanced tumor

rEA:

The region of entire abnormality

rNec:

The region of necrosis

TCGA:

The Cancer Genome Atlas

TCIA:

The Cancer Imaging Archive

TE:

Echo time

TR:

Repetition time

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Funding

This study has received funding by National Nature Science Foundation of China (No. 81701658 to Xi Zhang and No.81801655 to Qiang Tian), Military Science Foundation of China (No. BWS14J038 to Yang Liu), and National Key Research and Development Program of China (No. 2017YFC0107403 to Hongbing Lu).

Author information

Author notes

  1. Xi Zhang and Hongbing Lu contributed equally to this work.

Authors and Affiliations

  1. School of Biomedical Engineering, Fourth Military Medical University, No.169, Changle West Road, Xi’an, 710032, Shaanxi, People’s Republic of China

    Xi Zhang, Hongbing Lu, Lulu Yin, Xiaopan Xu, Peng Du & Yang Liu

  2. Department of Radiology, Tangdu Hospital, Fourth Military Medical University, 569 Xinsi Road, Xi’an, 710038, Shaanxi, People’s Republic of China

    Qiang Tian

  3. Department of Physiology (N.F.), Fourth Military Medical University, 169 Changle West Road, Xi’an, 710032, Shaanxi, People’s Republic of China

    Na Feng

Authors
  1. Xi Zhang
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  4. Na Feng
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Corresponding author

Correspondence to Yang Liu.

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Guarantor

The scientific guarantor of this publication is Hongbing Lu.

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

One of the authors, Xiaopan Xu, has significant statistical expertise.

Informed consent

Written informed consent was not required for this study because all the patient data in TCGA was deidentified.

Ethical approval

Institutional Review Board approval was not required because all the data used in this study were selected from the Cancer Genome Atlas (TCIA). After ethical review by NIH, the TCIA is freely available for the scientific research. Followed by the instructions of TCIA, we have referred related articles about TCIA.

Study subjects or cohorts overlap

Some study subjects or cohorts have been previously reported in AJNR (Am J Neuroradiol. 2017. https://doi.org/10.3174/ajnr.A5279).

Methodology

• retrospective

• diagnostic or prognostic study

• multicenter study

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Zhang, X., Lu, H., Tian, Q. et al. A radiomics nomogram based on multiparametric MRI might stratify glioblastoma patients according to survival. Eur Radiol 29, 5528–5538 (2019). https://doi.org/10.1007/s00330-019-06069-z

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