Advertisement

Radiological Physics and Technology

, Volume 11, Issue 1, pp 27–35 | Cite as

Classification of early stage non-small cell lung cancers on computed tomographic images into histological types using radiomic features: interobserver delineation variability analysis

  • Akihiro HagaEmail author
  • Wataru Takahashi
  • Shuri Aoki
  • Kanabu Nawa
  • Hideomi Yamashita
  • Osamu Abe
  • Keiichi Nakagawa
Article

Abstract

Radiomics, which involves the extraction of large numbers of quantitative features from medical images, has attracted attention in cancer research. In radiomics analysis, tumor segmentation is a crucial step. In this study, we evaluated the potential application of radiomics for predicting the histology of early stage non-small cell lung cancer (NSCLC) by analyzing interobserver variability in tumor delineation. Forty patient datasets were included in this study, 21 involving adenocarcinomas and 19 involving squamous cell carcinomas. All patients underwent stereotactic body radiotherapy treatment. In total, 476 features were extracted from each dataset, representing treatment planning, computed tomography images, and gross tumor volume (GTV). The definition of GTV can significantly affect the histology prediction. Therefore, in the present study, the effect of interobserver tumor delineation variability on radiomic features was evaluated by preparing 4 volumes of interest (VOIs) for each patient, as follows: the original GTV (which was delineated at treatment planning); two GTVs delineated retrospectively by radiation oncologists; and a semi-automatic GTV contoured by a medical physicist. Radiomic features extracted from each VOI were then analyzed using a naïve Bayesian model. Area-under-the-curve (AUC) analysis showed that interobserver variability in delineation is a significant factor in radiomics performance. Nevertheless, with 8 selected features, AUC values averaged over the VOIs were high (0.725 ± 0.070). The present study indicated that radiomics has potential for predicting early stage NSCLC histology despite variability in delineation. The high prediction accuracy implies that noninvasive histology evaluation by radiomics is a promising clinical application.

Keywords

Radiomics Histology Non-small-cell lung cancer (NSCLC) Prediction Machine learning 

Notes

Compliance with ethical standards

Conflict of interest

The authors declare that they have no competing interests.

Funding

This work was partially supported by a Grant-in-Aid from the Japan Society for the Promotion of Science (JSPS) KAKENHI JP Scientific Research (C), Grant number 15K08691.

Ethical approval

The present study is ethically approved by institutional review board in the University of Tokyo Hospital. The reference number is 3372. This article does not involve any studies performed with animals.

Informed consent

Written informed consent was obtained from all patients whose data were used in this study.

Supplementary material

12194_2017_433_MOESM1_ESM.docx (1.2 mb)
Supplementary material 1 (DOCX 1231 kb)

References

  1. 1.
    Yip SSF, Aerts HJWL. Applications and limitations of radiomics. Phys Med Biol. 2016;61(13):R150–66.CrossRefPubMedPubMedCentralGoogle Scholar
  2. 2.
    Kumar V, Gu Y, Basu S, Berglund A, Eschrich SA, Schabath MB, et al. Radiomics: the process and the challenges. Magn Reson Imaging. 2012;30(9):1234–48.CrossRefPubMedPubMedCentralGoogle Scholar
  3. 3.
    Aerts HJ, Velazquez ER, Leijenaar RT, Parmar C, Grossmann P, Carvalho S, et al. Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach. Nat Commun. 2014;5:4006.PubMedPubMedCentralGoogle Scholar
  4. 4.
    Lambin P, Rios-Velazquez E, Leijenaar R, Carvalho S, Van Stiphout RGPM, Granton P, et al. Radiomics: extracting more information from medical images using advanced feature analysis. Eur J Cancer. 2012;48(4):441–6.CrossRefPubMedPubMedCentralGoogle Scholar
  5. 5.
    Yamamoto S, Korn RL, Oklu R, Migdal C, Gotway MB, Weiss GJ, et al. ALK molecular phenotype in non-small cell lung cancer: CT radiogenomic characterization. Radiology. 2014;272(2):568–76.CrossRefPubMedGoogle Scholar
  6. 6.
    Ellingson BM, Lai A, Harris RJ, Selfridge JM, Yong WH, Das K, et al. Probabilistic radiographic atlas of glioblastoma phenotypes. Am J Neuroradiol. 2013;34(3):533–40.CrossRefPubMedGoogle Scholar
  7. 7.
    Boellaard R. Need for standardization of 18F-FDG PET/CT for treatment response assessments. J Nucl Med. 2011;52(Suppl 2):93S–100S.CrossRefPubMedGoogle Scholar
  8. 8.
    King AD, Chow K-K, Yu K-H, Mo FKF, Yeung DKW, Yuan J, et al. Head and neck squamous cell carcinoma: diagnostic performance of diffusion-weighted MR imaging for the prediction of treatment response. Radiology. 2013;266(2):531–8.CrossRefPubMedGoogle Scholar
  9. 9.
    Baek HJ, Kim HS, Kim N, Choi YJ, Kim YJ. Percent change of perfusion skewness and kurtosis: a potential imaging biomarker for early treatment response in patients with newly diagnosed glioblastomas (histographic pattern). Radiology. 2012;264(3):834–43.CrossRefPubMedGoogle Scholar
  10. 10.
    Xu R, Kido S, Suga K, Hirano Y, Tachibana R, Muramatsu K, et al. Texture analysis on 18F-FDG PET/CT images to differentiate malignant and benign bone and soft-tissue lesions. Ann Nucl Med. 2014;28(9):926–35.CrossRefPubMedGoogle Scholar
  11. 11.
    Nie K, Chen JH, Yu HJ, Chu Y, Nalcioglu O, Su MY. Quantitative analysis of lesion morphology and texture features for diagnostic prediction in breast MRI. Acad Radiol AUR. 2008;15(12):1513–25.CrossRefGoogle Scholar
  12. 12.
    Wu W, Parmar C, Grossmann P, Quackenbush J, Lambin P, Bussink J, et al. Exploratory study to identify radiomics classifiers for lung cancer histology. Front Oncol. 2016;6:71.PubMedPubMedCentralGoogle Scholar
  13. 13.
    Parmar C, Velazquez ER, Leijenaar R, Jermoumi M, Carvalho S, Mak RH, et al. Robust radiomics feature quantification using semiautomatic volumetric segmentation. PLoS One. 2014;9(7):1–8.CrossRefGoogle Scholar
  14. 14.
    Gaede S, Olsthoorn J, Louie AV, Palma D, Yu E, Yaremko B, et al. An evaluation of an automated 4D-CT contour propagation tool to define an internal gross tumour volume for lung cancer radiotherapy. Radiother Oncol. 2011;101(2):322–8.CrossRefPubMedGoogle Scholar
  15. 15.
    Haralick R, Shanmugan K, Dinstein I. Textural features for image classification. IEEE Trans Syst Man Cybern. 1973;1973:610–21.CrossRefGoogle Scholar
  16. 16.
    Galloway MM. Texture analysis using gray level run lengths. Comput Graph Image Process. 1975;4(2):172–9.CrossRefGoogle Scholar
  17. 17.
    Chu A, Sehgal CM, Greenleaf JF. Use of gray value distribution of run lengths for texture analysis. Pattern Recognit Lett. 1990;11(6):415–9.CrossRefGoogle Scholar
  18. 18.
    Dasarathy BV, Holder EB. Image characterizations based on joint gray level-run length distributions. Pattern Recognit Lett. 1991;12(8):497–502.CrossRefGoogle Scholar
  19. 19.
    Thibault G, Fertil B, Navarro C, Pereira S, Cau P, Levy N, Sequeira J and Mari J-L. Texture indexes and gray level size zone matrix: application to cell nuclei classification. In: 10th International conference on pattern recognition and information processing. Minsk; 2009. pp. 140–145.Google Scholar
  20. 20.
    Amadasun M, King R. Textural features corresponding to textural properties. IEEE Trans Syst Man Cybern. 1989;19(5):1264–73.CrossRefGoogle Scholar
  21. 21.
    Vallières M, Freeman CR, Skamene SR, El Naqa I. 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. 2015;60(14):5471–96.CrossRefPubMedGoogle Scholar
  22. 22.
    Ratanamahatana C, Gunopulos D. Feature selection for the naive bayesian classifier using decision trees. Appl Artif Intell. 2003;17(5–6):475–87.CrossRefGoogle Scholar
  23. 23.
    Bishop CM. Pattern recognition and machine learning, pattern recognition. Berlin: Springer; 2006. p. 791–9.Google Scholar
  24. 24.
    Schmidhuber J. Deep learning in neural networks: an overview. Neural Netw. 2015;61:85–117.CrossRefPubMedGoogle Scholar
  25. 25.
    Paez JG, Janne PA, Lee JC, Tracy S, Greulich H, Gabriel S, et al. EGFR mutations in lung cancer: correlation with clinical response to gefitinib therapy. Science. 2004;304(5676):1497–500.CrossRefPubMedGoogle Scholar

Copyright information

© Japanese Society of Radiological Technology and Japan Society of Medical Physics 2017

Authors and Affiliations

  • Akihiro Haga
    • 1
    Email author
  • Wataru Takahashi
    • 1
  • Shuri Aoki
    • 1
  • Kanabu Nawa
    • 1
  • Hideomi Yamashita
    • 1
  • Osamu Abe
    • 1
  • Keiichi Nakagawa
    • 1
  1. 1.Department of RadiologyThe University of Tokyo HospitalTokyoJapan

Personalised recommendations