Open AccessRadiomic features analysis in computed tomography images of lung nodule classification
Author(s) -
Chia-Hung Chen,
Chih Kun Chang,
Chih Yen Tu,
Wei Liao,
Bing Wu,
Kuei Ting Chou,
Yu Rou Chiou,
Shih Neng Yang,
Geoffrey Zhang,
Tzung Chi Huang
Publication year - 2018
Publication title -
plos one
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.99
H-Index - 332
ISSN - 1932-6203
DOI - 10.1371/journal.pone.0192002
Subject(s) - radiomics , radiology , lung cancer , medicine , nodule (geology) , medical imaging , solitary pulmonary nodule , lung , computed tomography , artificial intelligence , pathology , computer science , biology , paleontology
Purpose Radiomics, which extract large amount of quantification image features from diagnostic medical images had been widely used for prognostication, treatment response prediction and cancer detection. The treatment options for lung nodules depend on their diagnosis, benign or malignant. Conventionally, lung nodule diagnosis is based on invasive biopsy. Recently, radiomics features, a non-invasive method based on clinical images, have shown high potential in lesion classification, treatment outcome prediction. Methods Lung nodule classification using radiomics based on Computed Tomography (CT) image data was investigated and a 4-feature signature was introduced for lung nodule classification. Retrospectively, 72 patients with 75 pulmonary nodules were collected. Radiomics feature extraction was performed on non-enhanced CT images with contours which were delineated by an experienced radiation oncologist. Result Among the 750 image features in each case, 76 features were found to have significant differences between benign and malignant lesions. A radiomics signature was composed of the best 4 features which included Laws_LSL_min, Laws_SLL_energy, Laws_SSL_skewness and Laws_EEL_uniformity. The accuracy using the signature in benign or malignant classification was 84% with the sensitivity of 92.85% and the specificity of 72.73%. Conclusion The classification signature based on radiomics features demonstrated very good accuracy and high potential in clinical application.