Radiomic signature as a diagnostic factor for histologic subtype classification of non-small cell lung cancer
- 1.1k Downloads
To distinguish squamous cell carcinoma (SCC) from lung adenocarcinoma (ADC) based on a radiomic signature
This study involved 129 patients with non-small cell lung cancer (NSCLC) (81 in the training cohort and 48 in the independent validation cohort). Approximately 485 features were extracted from a manually outlined tumor region. The LASSO logistic regression model selected the key features of a radiomic signature. Receiver operating characteristic curve and area under the curve (AUC) were used to evaluate the performance of the radiomic signature in the training and validation cohorts.
Five features were selected to construct the radiomic signature for histologic subtype classification. The performance of the radiomic signature to distinguish between lung ADC and SCC in both training and validation cohorts was good, with an AUC of 0.905 (95% confidence interval [CI]: 0.838 to 0.971), sensitivity of 0.830, and specificity of 0.929. In the validation cohort, the radiomic signature showed an AUC of 0.893 (95% CI: 0.789 to 0.996), sensitivity of 0.828, and specificity of 0.900.
A unique radiomic signature was constructed for use as a diagnostic factor for discriminating lung ADC from SCC. Patients with NSCLC will benefit from the proposed radiomic signature.
• Machine learning can be used for auxiliary distinguish in lung cancer.
• Radiomic signature can discriminate lung ADC from SCC.
• Radiomics can help to achieve precision medical treatment.
KeywordsAenocarcinoma Diagnostic imaging Regression analysis ROC curve Squamous cell carcinoma
Area Under the Curve
Least Absolute Shrinkage and Selection Operator
Non-small Cell Lung Cancer
Positron Emission Tomography
Receiver Operating Characteristic
Region of Interest
Squamous Cell Carcinoma
This work was supported by the National Natural Science Foundation of China (81227901, 81771924, 81501616, 61231004, 81671851, and 81527805), National Key R&D Program of China (2017YFA0205200, 2017YFC1308700, 2017YFC1308701, 2017YFC1309100), the Science and Technology Service Network Initiative of the Chinese Academy of Sciences (KFJ-SW-STS-160), the Instrument Developing Project of the Chinese Academy of Sciences (YZ201502), the Beijing Municipal Science and Technology Commission (Z161100002616022), and the Youth Innovation Promotion Association CAS.
Compliance with ethical standards
The scientific guarantor of this publication is Jie Tian.
Conflict of interest
The authors of this manuscript declare no relationships with any companies, whose products or services may be related to the subject matter of the article.
Statistics and biometry
No complex statistical methods were necessary for this paper.
Written informed consent was obtained from all subjects (patients) in this study.
Institutional Review Board approval was obtained.
• diagnostic or prognostic study
• performed at one institution
- 6.Fukui T, Taniguchi T, Kawaguchi K et al (2015) Comparisons of the clinicopathological features and survival outcomes between lung cancer patients with adenocarcinoma and squamous cell carcinoma. Gen Thorac Cardiovasc Surg 63:507–513Google Scholar
- 9.Herbst RS, O'Neill VJ, Fehrenbacher L et al (2007) Phase II study of efficacy and safety of bevacizumab in combination with chemotherapy or erlotinib compared with chemotherapy alone for treatment of recurrent or refractory non small-cell lung cancer. J Clin Oncol 25:4743–4750CrossRefPubMedGoogle Scholar
- 11.Nguyenkim TD, Frauenfelder T, Strobel K et al (2015) Assessment of Bronchial and Pulmonary Blood Supply in Non-Small Cell Lung Cancer Subtypes Using Computed Tomography Perfusion. Investig Radiol 50:179–186Google Scholar
- 16.Ganeshan B, Skogen K, Pressney I, Coutroubis D and MilesK (2012) Tumour heterogeneity in oesophageal cancer assessed by CT texture analysis: preliminary evidence of an association with tumour metabolism, stage, and survival. Clin Radiol 67:157-164.Google Scholar
- 25.Huang Y, Liu Z, He L et al (2016) Radiomics Signature: A Potential Biomarker for the Prediction of Disease-Free Survival in Early-Stage (I or II) Non-Small Cell Lung Cancer. Radiology 281:152234Google Scholar
- 29.Chi MA, Jian H (2016) Asymptotic properties of LASSO in high-dimensional partially linear models. Science China Mathematics 59:1–20Google Scholar
- 32.Ball DL, Fisher RJ, Burmeister BH et al (2013) The complex relationship between lung tumor volume and survival in patients with non-small cell lung cancer treated by definitive radiotherapy: A prospective, observational prognostic factor study of the Trans-Tasman Radiation Oncology Group (TROG 99.05). Radiother Oncol 106:305–311CrossRefPubMedGoogle Scholar