European Radiology

, Volume 29, Issue 4, pp 1841–1847 | Cite as

Comparison of radiomics machine-learning classifiers and feature selection for differentiation of sacral chordoma and sacral giant cell tumour based on 3D computed tomography features

  • Ping Yin
  • Ning Mao
  • Chao Zhao
  • Jiangfen Wu
  • Chao Sun
  • Lei Chen
  • Nan HongEmail author



We aimed to identify optimal machine-learning methods for preoperative differentiation of sacral chordoma (SC) and sacral giant cell tumour (SGCT) based on 3D non-enhanced computed tomography (CT) and CT-enhanced (CTE) features.


A total of 95 patients were divided into a training set and a validation set. Three best feature selection methods (Relief, least absolute shrinkage and selection operator (LASSO) and Random Forest (RF)) and three classification methods, including generalised linear models (GLM), support vector machines (SVM) and RF, were compared for their performance in distinguishing SC and SGCT. The performance of the radiomics model was investigated via area under the receiver-operating characteristic curve (AUC) and accuracy (ACC) analysis.


The selection method LASSO + classifier GLM had the highest AUC of 0.984 and ACC of 0.897 in the validating set, followed by Relief + GLM (AUC = 0.909, ACC = 0.862) and LASSO + SVM (AUC = 0.900, ACC = 0.862) based on CTE features. For CT features, RF + GLM had the highest AUC of 0.889, while LASSO + GLM achieved a high ACC of 0.793 in the validating set. Regardless of the methods, CTE features significantly outperformed those from CT for the differentiation of SC and SGCT (ZAUC = -3.029, ZACC = -4.553; p < 0.05).


Our study demonstrated CTE features performed better than CT features. The selection method LASSO + classifier GLM had the best performance in differentiation of SC and SGCT, which could enhance the application of radiomics methods in sacral tumours.

Key Points

• Sacral chordoma and sacral giant cell tumour are the two most common primary tumours of the sacrum with many common clinical and imaging characteristics.

• A radiomics model helps clinicians to identify the histology of a sacral tumour.

• CTE features should be preferred.


Sacrum Bone neoplasms Algorithms Machine learning 





Area under the receiver-operating characteristic curve


Computed tomography


Computed tomography enhanced


Field of view


Generalised linear models


Intra- and interclass correlation coefficients


Least absolute shrinkage and selection operator


Multi-detector row CT


Picture archiving and communication system


Random Forest


Regions of interest


Sacral chordoma


Sacral giant cell tumour


Support vector machines



The authors state that this work has not received any funding.

Compliance with ethical standards


The scientific guarantor of this publication is Jiangfen Wu.

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

One of the authors has significant statistical expertise.

Informed consent

Written informed consent was waived by the Institutional Review Board.

Ethical approval

Institutional Review Board approval was obtained.


• Retrospective

• Diagnostic or prognostic study

• Performed at one institution


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Copyright information

© European Society of Radiology 2018

Authors and Affiliations

  1. 1.Department of RadiologyPeking University People’s HospitalBeijingPeople’s Republic of China
  2. 2.Department of RadiologyQindao University Medical College Affiliated Yantai Yuhuangding HospitalYantaiPeople’s Republic of China
  3. 3.GE HealthcareShanghaiPeople’s Republic of China

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