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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

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

Abstract

Objective

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.

Methods

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.

Results

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).

Conclusions

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.

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Abbreviations

ACC:

Accuracy

AUC:

Area under the receiver-operating characteristic curve

CT:

Computed tomography

CTE:

Computed tomography enhanced

FOV:

Field of view

GLM:

Generalised linear models

ICC:

Intra- and interclass correlation coefficients

LASSO:

Least absolute shrinkage and selection operator

MDCT:

Multi-detector row CT

PACS:

Picture archiving and communication system

RF:

Random Forest

ROIs:

Regions of interest

SC:

Sacral chordoma

SGCT:

Sacral giant cell tumour

SVM:

Support vector machines

References

  1. Thornton E, Krajewski KM, O'Regan KN, Giardino AA, Jagannathan JP, Ramaiya N (2012) Imaging features of primary and secondary malignant tumours of the sacrum. Br J Radiol 85(1011):279–286

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  2. Gerber S, Ollivier L, Leclère J et al (2008) Imaging of sacral tumours. Skeletal Radiol 37(4):277–289

    Article  CAS  PubMed  Google Scholar 

  3. Ji T, Guo W, Yang R, Tang X, Wang Y, Huang L (2017) What are the conditional survival and functional outcomes after surgical treatment of 115 patients with sacral chordoma? Clin Orthop Relat Res 475(3):620–630

    Article  PubMed  Google Scholar 

  4. Si MJ, Wang CS, Ding XY et al (2013) Differentiation of primary chordoma, giant cell tumor and schwannoma of the sacrum by CT and MRI. Eur J Radiol 82(12):2309–2315

    Article  PubMed  Google Scholar 

  5. Jamshidi K, Bagherifard A, Mirzaei A, Bahrabadi M (2017) Giant cell tumor of the sacrum: series of 19 patients and review of the literature. Arch Bone Jt Surg 5(6):443–450

    PubMed  PubMed Central  Google Scholar 

  6. Avanzo M, Stancanello J, El Naqa I (2017) Beyond imaging: the promise of radiomics. Phys Med 38:122–139

    Article  PubMed  Google Scholar 

  7. Ruosi C, Colella G, Di Donato SL, Granata F, Di Salvatore MG, Fazioli F (2015) Surgical treatment of sacral chordoma: survival and prognostic factors. Eur Spine J 24(Suppl 7):912–917

    Article  PubMed  Google Scholar 

  8. Vallières M, Freeman CR, Skamene SR, El Naqa I (2015) A radiomics model from joint FDG-PET and MRI texture features for the prediction of lung metastases in soft-tissue sarcomas of the extremities. Phys Med Biol 60:5471–5496

    Article  PubMed  Google Scholar 

  9. Shen C, Liu Z, Guan M et al (2017) 2D and 3D CT radiomics features prognostic performance comparison in non-small cell lung cancer. Transl Oncol 10(6):886–894

    Article  PubMed  PubMed Central  Google Scholar 

  10. Bowen SR, Yuh WTC, Hippe DS et al (2018) Tumor radiomic heterogeneity: multiparametric functional imaging to characterize variability and predict response following cervical cancer radiation therapy. J Magn Reson Imaging 47(5):1388–1396

    Article  PubMed  Google Scholar 

  11. Larue RT, Defraene G, De Ruysscher D, Lambin P, van Elmpt W (2017) Quantitative radiomics studies for tissue characterization: a review of technology and methodological procedures. Br J Radiol 90(1070):20160665

    Article  PubMed  PubMed Central  Google Scholar 

  12. Yip SS, Aerts HJ (2016) Applications and limitations of radiomics. Phys Med Biol 61(13):R150–R166

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Lambin P, Leijenaar RTH, Deist TM et al (2017) Radiomics: the bridge between medical imaging and personalized medicine. Nat Rev Clin Oncol 14(12):749–762

    Article  PubMed  Google Scholar 

  14. Kickingereder P, Burth S, Wick A et al (2016) Radiomic profiling of glioblastoma: identifying an imaging predictor of patient survival with improved performance over established clinical and radiologic risk models. Radiology 280(3):880–889

    Article  PubMed  Google Scholar 

  15. Liu J, Mao Y, Li Z et al (2016) Use of texture analysis based on contrast-enhanced MRI to predict treatment response to chemoradiotherapy in nasopharyngeal carcinoma. J Magn Reson Imaging 44:445–455

    Article  PubMed  Google Scholar 

  16. Lubner MG, Smith AD, Sandrasegaran K, Sahani DV, Pickhardt PJ (2017) CT texture analysis: definitions, applications, biologic correlates, and challenges. Radiographics 37(5):1483–1503

    Article  PubMed  Google Scholar 

  17. Cha KH, Hadjiiski L, Chan HP et al (2017) Bladder cancer treatment response assessment in CT using radiomics with deep-learning. Sci Rep 7(1):8738

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Pota M, Scalco E, Sanguineti G et al (2017) Early prediction of radiotherapy-induced parotid shrinkage and toxicity based on CT radiomics and fuzzy classification. Artif Intell Med 81:41–53

    Article  PubMed  Google Scholar 

  19. Bashir U, Azad G, Siddique MM et al (2017) The effects of segmentation algorithms on the measurement of 18F-FDG PET texture parameters in non-small cell lung cancer. EJNMMI Res 7(1):60

    Article  PubMed  PubMed Central  Google Scholar 

  20. Tixier F, Groves AM, Goh V et al (2014) Correlation of intra-tumor (18)F-FDG uptake heterogeneity indices with perfusion CT derived parameters in colorectal cancer. PLoS One 9:e99567

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    PubMed  PubMed Central  Google Scholar 

  22. Parmar C, Grossmann P, Bussink J, Lambin P, Aerts HJ (2015) Machine learning methods for quantitative radiomic biomarkers. Sci Rep 5:13087

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Zhang B, He X, Ouyang F et al (2017) Radiomic machine-learning classifiers for prognostic biomarkers of advanced nasopharyngeal carcinoma. Cancer Lett 403:21–27

    Article  CAS  PubMed  Google Scholar 

  24. Wu S, Zheng J, Li Y et al (2017) A radiomics nomogram for the preoperative prediction of lymph node metastasis in bladder cancer. Clin Cancer Res 23(22):6904–6911

    Article  CAS  PubMed  Google Scholar 

  25. Zhu X, Dong D, Chen Z et al (2018) Radiomic signature as a diagnostic factor for histologic subtype classification of non-small cell lung cancer. Eur Radiol. https://doi.org/10.1007/s00330-017-5221-1

  26. Sun Y, Hu P, Wang J et al (2018) Radiomic features of pretreatment MRI could identify T stage in patients with rectal cancer: Preliminary findings. J Magn Reson Imaging. https://doi.org/10.1002/jmri.25969

  27. Hawkins SH, Korecki JN, Balagurunathan Y et al (2014) Predicting outcomes of nonsmall cell lung cancer using CT image features. IEEE Access 2:1418–1426

    Article  Google Scholar 

  28. Parmar C, Grossmann P, Rietveld D, Rietbergen MM, Lambin P, Aerts HJ (2015) Radiomic machine-learning classifiers for prognostic biomarkers of head and neck cancer. Front Oncol 5:272

    Article  PubMed  PubMed Central  Google Scholar 

  29. Zhang B, Tian J, Dong D et al (2017) Radiomics features of multiparametric MRI as novel prognostic factors in advanced nasopharyngeal carcinoma. Clin Cancer Res 23(15):4259–4269

    Article  PubMed  Google Scholar 

  30. Yuan M, Zhang YD, Pu XH et al (2017) Comparison of a radiomic biomarker with volumetric analysis for decoding tumour phenotypes of lung adenocarcinoma with different disease-specific survival. Eur Radiol 27(11):4857–4865

    Article  PubMed  Google Scholar 

  31. Asl BM, Setarehdan SK, Mohebbi M (2008) Support vector machine-based arrhythmia classification using reduced features of heart rate variability signal. Artif Intell Med 44(1):51–64

    Article  PubMed  Google Scholar 

  32. Parekh V, Jacobs MA (2016) Radiomics: a new application from established techniques. Expert Rev Precis Med Drug Dev 1(2):207–226

    Article  PubMed  PubMed Central  Google Scholar 

  33. Breiman L (2001) Random forests. Mach Learn 45(1):5–32

    Article  Google Scholar 

  34. Fernández-Delgado M, Cernadas E, Barro S, Amorim D (2014) Do we need hundreds of classifiers to solve real world classification problems? J Mach Learn Res:3133-3181

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Funding

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

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Authors and Affiliations

  1. Department of Radiology, Peking University People’s Hospital, 11 Xizhimen Nandajie, Xicheng District, Beijing, 100044, People’s Republic of China

    Ping Yin, Ning Mao, Chao Zhao, Chao Sun, Lei Chen & Nan Hong

  2. Department of Radiology, Qindao University Medical College Affiliated Yantai Yuhuangding Hospital, Yantai, Shandong, People’s Republic of China

    Ning Mao

  3. GE Healthcare, Shanghai, People’s Republic of China

    Jiangfen Wu

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  1. Ping Yin
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  6. Lei Chen
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  7. Nan Hong
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Correspondence to Nan Hong.

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The scientific guarantor of this publication is Jiangfen Wu.

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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.

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Written informed consent was waived by the Institutional Review Board.

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Institutional Review Board approval was obtained.

Methodology

• Retrospective

• Diagnostic or prognostic study

• Performed at one institution

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Yin, P., Mao, N., Zhao, C. et al. 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. Eur Radiol 29, 1841–1847 (2019). https://doi.org/10.1007/s00330-018-5730-6

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  • DOI: https://doi.org/10.1007/s00330-018-5730-6

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