Radiomic signature as a diagnostic factor for histologic subtype classification of non-small cell lung cancer

• Published in: European radiology Vol. 28; no. 7; pp. 2772 - 2778
• Main Authors: Zhu, Xinzhong, Dong, Di, Chen, Zhendong, Fang, Mengjie, Zhang, Liwen, Song, Jiangdian, Yu, Dongdong, Zang, Yali, Liu, Zhenyu, Shi, Jingyun, Tian, Jie
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.07.2018; Springer Nature B.V
• Subjects: Adenocarcinoma; Adenocarcinoma - diagnostic imaging; Adenocarcinoma of Lung; Adult; Aged; Area Under Curve; Cancer; Carcinoma, Non-Small-Cell Lung - diagnostic imaging; Carcinoma, Non-Small-Cell Lung - pathology; Carcinoma, Squamous Cell - diagnostic imaging; Carcinoma, Squamous Cell - pathology; Classification; Computed Tomography; Confidence intervals; Diagnosis, Differential; Diagnostic Radiology; Diagnostic systems; Feature extraction; Female; Humans; Imaging; Internal Medicine; Interventional Radiology; Learning algorithms; Lung cancer; Lung carcinoma; Lung Neoplasms - diagnostic imaging; Lung Neoplasms - pathology; Machine learning; Male; Medical diagnosis; Medical treatment; Medicine; Medicine & Public Health; Middle Aged; Neuroradiology; Non-small cell lung carcinoma; Patients; Performance evaluation; Radiographic Image Interpretation, Computer-Assisted - methods; Radiology; Radiomics; Regression models; Retrospective Studies; ROC Curve; Sensitivity; Sensitivity and Specificity; Small cell lung carcinoma; Squamous cell carcinoma; Statistical analysis; Tomography, X-Ray Computed - methods; Training; Ultrasound; Aenocarcinoma; Squamous cell carcinoma; Diagnostic imaging; Regression analysis; ROC curve
• ISSN: 0938-7994; 1432-1084; 1432-1084
• DOI: 10.1007/s00330-017-5221-1

Objectives To distinguish squamous cell carcinoma (SCC) from lung adenocarcinoma (ADC) based on a radiomic signature Methods 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. Results 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. Conclusions 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. Key points • 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.