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


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.


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


Compliance with ethical standards

Conflict of interest

The authors declare that they have no competing interests.


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)


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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

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