Skip to main content

Advertisement

SpringerLink
Log in

Intratumoral and peritumoral CT-based radiomics strategy reveals distinct subtypes of non-small-cell lung cancer

  • Original Article – Cancer Research
  • Published:
Journal of Cancer Research and Clinical Oncology Aims and scope Submit manuscript

Abstract

Purpose

To evaluate a new radiomics strategy that incorporates intratumoral and peritumoral features extracted from lung CT images with ensemble learning for pretreatment prediction of lung squamous cell carcinoma (LUSC) and lung adenocarcinoma (LUAD).

Methods

A total of 105 patients (47 LUSC and 58 LUAD) with pretherapy CT scans were involved in this retrospective study, and were divided into training (n = 73) and testing (n = 32) cohorts. Seven categories of radiomics features involving 3078 metrics in total were extracted from the intra- and peritumoral regions of each patient’s CT data. Student’s t tests in combination with three feature selection methods were adopted for optimal features selection. An ensemble classifier was developed using five common machine learning classifiers with these optimal features. The performance was assessed using both training and testing cohorts, and further compared with that of Visual Geometry Group-16 (VGG-16) deep network for this predictive task.

Results

The classification models developed using optimal feature subsets determined from intratumoral region and peritumoral region with the ensemble classifier achieved mean area under the curve (AUC) of 0.87, 0.83 in the training cohort and 0.66, 0.60 in the testing cohort, respectively. The model developed by using the optimal feature subset selected from both intra- and peritumoral regions with the ensemble classifier achieved great performance improvement, with AUC of 0.87 and 0.78 in both cohorts, respectively, which are also superior to that of VGG-16 (AUC of 0.68 in the testing cohort).

Conclusions

The proposed new radiomics strategy that extracts image features from the intra- and peritumoral regions with ensemble learning could greatly improve the diagnostic performance for the histological subtype stratification in patients with NSCLC.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

Similar content being viewed by others

Data availability statement

The raw/processed data of this study cannot be publicly shared at present as it forms part of an ongoing study, but it could be available under reasonable request from the corresponding author with the permission of the Institutional Review Board. Results and code package in each step of this study have been arranged in a document named as “Appendix”. The code package has also been uploaded to Gitee for publicly sharing and further perfection (https://gitee.com/yang-tianran-01/radiomics_-ensemble_learning/commit/d51e6859ef48c92cc0c794639f08286ac89569f8).

Abbreviations

AUC:

Area under the curve

CM:

Co-occurrence matrices

CNN:

Convolutional neural network

CT:

Computed tomography

FN:

False negative

FP:

False positive

GLCM:

Gray-level co-occurrence matrix

GLDM:

Gray-level dependence matrix

GLRLM:

Gray-level run length matrix

GLSZM:

Gray-level size zone matrix

LASSO:

Least absolute shrinkage and selection operator

LBP:

Local binary pattern

LUAD:

Lung adenocarcinoma

LUSC:

Lung squamous cell carcinoma

MID:

Mutual information difference

mpMRI:

Multiparametric magnetic resonance imaging

mRMR:

Minimum redundancy maximum relevance

NGTDM:

Neighboring gray-tone difference matrix

NSCLC:

Nonsmall-cell lung cancer

PET-CT:

Positron emission tomography computed tomography

QDA:

Quadratic discriminant analysis

RBF:

Radial basis function

RF:

Random forest

RLM:

Run length matrix

ROC:

Receiver-operating characteristic curve

SVM:

Support vector machine

SVM-RFE:

Support vector machine-based recursive feature elimination

TN:

True negative

TP:

True positive

VGG:

Visual geometry group network

XGBoost:

Extreme gradient boosting

References

  • Amadasun M, King R (1989) Texural features corresponding to texural properties. IEEE Trans Syst Man Cybern 19(5):1264–1274

    Article  Google Scholar 

  • Bashir U et al (2019) Non-invasive classifcation of non-small cell lung cancer: a comparison between random forest models utilising radiomic and semantic features. Br J Radiol 92(20190159):1–8

    Google Scholar 

  • Beig N et al (2019) Perinodular and intranodular radiomic features on lung CT images distinguish adenocarcinomas from granulomas. Radiology 290(3):783–792

    Article  PubMed  Google Scholar 

  • Bray F et al (2018) Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. Cancer J Clin. https://doi.org/10.3322/caac.21492

    Article  Google Scholar 

  • Chaunzwa TL et al (2018) Using deep-learning radiomics to predict lung cancer histology. J Clin Oncol 36(15_Suppl):8545–8545

    Article  Google Scholar 

  • Chen T, Guestrin C (2016) XGBoost: a scalable tree boosting system. In: 2016. Association for Computing Machinery

  • Colen RR et al (2021) Radiomics analysis for predicting pembrolizumab response in patients with advanced rare cancers. J Immunother Cancer 9(4):1752

    Article  Google Scholar 

  • de Jong EEC et al (2018) Applicability of a prognostic CT-based radiomic signature model trained on stage I-III non-small cell lung cancer in stage IV non-small cell lung cancer. Lung Cancer 124:6–11

    Article  PubMed  Google Scholar 

  • Ebrahimi M et al (2016) Diagnostic concordance of non–small cell lung carcinoma subtypes between biopsy and cytology specimens obtained during the same procedure. Cancer Cytopathol 124(10):737–743

    Article  PubMed  Google Scholar 

  • Fehr D et al (2015) Automatic classification of prostate cancer Gleason scores from multiparametric magnetic resonance images. Proc Natl Acad Sci 112(46):E6265–E6273

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Galloway MM (1975) Texture analysis using gray level run lengths. Comput Graph Image Process 4:172–179

    Article  Google Scholar 

  • Gupta V, Mittal M (2019a) R-peak detection in ECG signal using yule-walker and principal component analysis. IETE J Res 67:921–934

    Article  Google Scholar 

  • Gupta V, Mittal M (2019b) A comparison of ECG signal pre-processing using FrFT, FrWT and IPCA for improved analysis. IRBM 40:145–156

    Article  Google Scholar 

  • Gupta V, Mittal M (2019c) QRS complex detection using STFT, chaos analysis, and PCA in standard and real-time ECG databases. J Inst Eng (india) 100:489–497

    Google Scholar 

  • Haralick RM, Shanmugam K, Dinstein IH (1973) Textural features for image classification. IEEE Trans Syst Man Cybern 3(6):610–621

    Article  Google Scholar 

  • Hastie T, Tibshirani R, Friedman J (2009) The elements of statistical learning: data mining, inference, and prediction, 2nd edn. Springer Science & Business Media, Berlin, p 757

    Book  Google Scholar 

  • Herbst RS, Heymach JV, Lippman SM (2008) Lung cancer. N Engl J Med 359(13):1367–1380

    Article  CAS  PubMed  Google Scholar 

  • Hoffman PC, Mauer AM, Vokes EE (2000) Lung cancer. Lancet 355(9202):479–485

    Article  CAS  PubMed  Google Scholar 

  • Khalilia M, Chakraborty S, Popescu M (2011) Predicting disease risks from highly imbalanced data using random forest. BMC Med Inform Decis Mak 11(1):51

    Article  PubMed  PubMed Central  Google Scholar 

  • Kirienko M et al (2018) Prediction of disease-free survival by the PET/CT radiomic signature in non-small cell lung cancer patients undergoing surgery. Eur J Nucl Med Mol Imaging 45(2):207–217

    Article  PubMed  Google Scholar 

  • Kora P, Krishna KSR (2014) Myocardial infarction detection using magnitude squared coherence and support vector machine. Med Imaging. https://doi.org/10.1109/MedCom.2014.7006037

    Article  Google Scholar 

  • Koyasu S et al (2020) Usefulness of gradient tree boosting for predicting histological subtype and EGFR mutation status of non-small cell lung cancer on 18F FDG-PET/CT. Ann Nucl Med 34(1):49–57

    Article  CAS  PubMed  Google Scholar 

  • Lam H-K et al (2012) Computational intelligence and its applications: evolutionary computation, fuzzy logic, neural network and support vector machine techniques. World Scientific, London, p 318

    Book  Google Scholar 

  • Li S et al (2019) Predicting lung nodule malignancies by combining deep convolutional neural network and handcrafted features. Phys Med Biol 64(17):175012

    Article  PubMed  PubMed Central  Google Scholar 

  • Liang C et al (2018) A computer-aided diagnosis scheme of breast lesion classification using GLGLM and shape features: Combined-view and multi-classifiers. Phys Med 55:61–72

    Article  PubMed  Google Scholar 

  • Linear & Quadratic Discriminant Analysis · UC Business Analytics R Programming Guide. https://uc-r.github.io/discriminant_analysis

  • Ma Y et al (2018a) Intra-tumoural heterogeneity characterization through texture and colour analysis for differentiation of non-small cell lung carcinoma subtypes. Phys Med Biol 63(16):1658

    Article  CAS  Google Scholar 

  • Ma Y et al (2018b) Intra-tumoural heterogeneity characterization through texture and colour analysis for differentiation of non-small cell lung carcinoma subtypes. Phys Med Biol 63(16):1658

    Article  CAS  Google Scholar 

  • Mahon RN, Hugo GD, Weiss E (2019) Repeatability of texture features derived from magnetic resonance and computed tomography imaging and use in predictive models for non-small cell lung cancer outcome. Phys Med Biol 64:145007

    Article  Google Scholar 

  • Peng H, Long F, Ding C (2005) Feature selection based on mutual information criteria of max-dependency, max-relevance, and min-redundancy. IEEE Trans Pattern Anal Mach Intell 27(8):1226–1238

    Article  PubMed  Google Scholar 

  • Probable T (1992) Error of a mean. In: Kotz S, Johnson NL (eds) Breakthroughs in statistics: methodology and distribution. Springer, New York, pp 33–57

    Google Scholar 

  • Ren C et al (2020) Machine learning based on clinico-biological features integrated 18F-FDG PET/CT radiomics for distinguishing squamous cell carcinoma from adenocarcinoma of lung. Eur J Nucl Med Mol Imaging. https://doi.org/10.1007/s00259-020-05065-6

    Article  PubMed  PubMed Central  Google Scholar 

  • Sauerbrei W, Royston P, Binder H (2007) Selection of important variables and determination of functional form for continuous predictors in multivariable model building. Stat Med 26(30):5512–5528

    Article  PubMed  Google Scholar 

  • Seera M, Lim CP (2014) A hybrid intelligent system for medical data classification. Expert Syst Appl 41(5):2239–2249

    Article  Google Scholar 

  • Shen C 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 

  • Sollini M et al (2017) PET Radiomics in NSCLC: state of the art and a proposal for harmonization of methodology. Sci Rep 7(1):358

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Starkov P et al (2018) The use of texture-based radiomics CT analysis to predict outcomes in early-stage non-small cell lung cancer treated with stereotactic ablative radiotherapy. Br J Radiol 91(20180228):1–7

    Google Scholar 

  • Stenzinger A et al (2021) Artificial intelligence and pathology: from principles to practice and future applications in histomorphology and molecular profiling. Semin Cancer Biol. https://doi.org/10.1016/j.semcancer.2021.02.011

    Article  PubMed  Google Scholar 

  • Su R et al (2019) Identification of expression signatures for non-small-cell lung carcinoma subtype classification. Bioinformatics. https://doi.org/10.1093/bioinformatics/btz557

    Article  PubMed  PubMed Central  Google Scholar 

  • Sun C, Wee WG (1983) Neighboring gray level dependence matrix for texture classification. Compute vis Graph Image Process 23:341–352

    Article  Google Scholar 

  • Sun W et al (2018) Effect of machine learning methods on predicting NSCLC overall survival time based on Radiomics analysis. Radiat Oncol 13(1):197

    Article  PubMed  PubMed Central  Google Scholar 

  • Sung H et al (2021) Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. Cancer J Clin 71:209–249

    Article  Google Scholar 

  • Tang X et al (2020) Elaboration of a multimodal MRI-based radiomics signature for the preoperative prediction of the histological subtype in patients with non-small-cell lung cancer. BioMed Eng Online. https://doi.org/10.1186/s12938-019-0744-0

    Article  PubMed  PubMed Central  Google Scholar 

  • Tharwat A (2016) Linear vs quadratic discriminant analysis classifier: a tutorial. Int J Appl Pattern Recogn 3(2):145

    Article  Google Scholar 

  • Thibault G, Angulo J, Meyer F (2014) Advanced statistical matrices for texture characterization: application to cell classification. IEEE Trans Biomed Eng 61(3):630–637

    Article  PubMed  Google Scholar 

  • Tibshirani R (1996) Regression Shrinkage and Selection Via the Lasso. J Roy Stat Soc Ser B (methodol) 58(1):267–288

    Google Scholar 

  • van Griethuysen JJM et al (2017) Computational radiomics system to decode the radiographic phenotype. Can Res 77(21):e104–e107

    Article  CAS  Google Scholar 

  • Wu W et al (2016) Exploratory study to identify radiomics classifiers for lung cancer histology. Front Oncol. https://doi.org/10.3389/fonc.2016.00071

    Article  PubMed  PubMed Central  Google Scholar 

  • Wu S 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 

  • Wu S et al (2018) Development and validation of an MRI-based radiomics signature for the preoperative prediction of lymph node metastasis in bladder cancer. EBioMedicine 34:76–84

    Article  PubMed  PubMed Central  Google Scholar 

  • Xu X et al (2017a) Preoperative prediction of muscular invasiveness of bladder cancer with radiomic features on conventional MRI and its high-order derivative maps. Abdom Radiol (NY) 42(7):1896–1905

    Article  Google Scholar 

  • Xu X et al (2017b) Three-dimensional texture features from intensity and high-order derivative maps for the discrimination between bladder tumors and wall tissues via MRI. Int J Comput Assist Radiol Surg 12(4):645–656

    Article  PubMed  Google Scholar 

  • Xu X et al (2019) A predictive nomogram for individualized recurrence stratification of bladder cancer using multiparametric MRI and clinical risk factors. J Magn Resonance Imaging 50:1893–1904

    Article  Google Scholar 

  • Zhu X et al (2018) Radiomic signature as a diagnostic factor for histologic subtype classification of non-small cell lung cancer. Eur Radiol 28(7):1–7

    Article  Google Scholar 

  • Zwanenburg A et al (2020) The image biomarker standardization initiative: standardized quantitative radiomics for high-throughput image-based phenotyping. Radiology 295(2):328–338

    Article  PubMed  Google Scholar 

Download references

Funding

This work was funded by the National Natural Science Foundation of China (No. 81901698) and Young Eagle plan of High Ambition Project (No. 2020CYJHXXP).

Author information

Author notes

  1. Xing Tang and Haolin Huang have contributed equally to this work, therefore, they are co-first authors.

Authors and Affiliations

  1. Department of Radiology, Xijing Hospital, Air Force Medical University, No. 169 Changle West Road, Xi’an, 710032, Shaanxi, People’s Republic of China

    Xing Tang & Hong Yin

  2. School of Biomedical Engineering, Air Force Medical University, Xi’an, Shaanxi, People’s Republic of China

    Haolin Huang, Peng Du, Lijuan Wang & Xiaopan Xu

Authors
  1. Xing Tang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Haolin Huang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Peng Du
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Lijuan Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Hong Yin
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Xiaopan Xu
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

XX, XT, and HH contributed to the study concept, design, and data interpretation. XT contributed to the CT and clinical data collection. XT and HY contributed to the intratumoral region annotation. HH, XX and PD performed the peritumoral region extraction and radiomics feature calculation; XX, HH and XT contributed to the model construction and data analysis. XX, XT, and HH contributed to the manuscript drafting, editing and revision. All authors approve the final version of the manuscript for submission.

Corresponding author

Correspondence to Xiaopan Xu.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Ethical approval

This study was approved by the institutional ethics review board of Xijing Hospital, and informed content was waived.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Below is the link to the electronic supplementary material.

Supplementary file1 (DOCX 119 KB)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Tang, X., Huang, H., Du, P. et al. Intratumoral and peritumoral CT-based radiomics strategy reveals distinct subtypes of non-small-cell lung cancer. J Cancer Res Clin Oncol 148, 2247–2260 (2022). https://doi.org/10.1007/s00432-022-04015-z

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00432-022-04015-z

Keywords

Search

Navigation