Chen Chia-Hung, Chang Chih-Kun, Tu Chih-Yen, Liao Wei-Chih, Wu Bing-Ru, Chou Kuei-Ting, Chiou Yu-Rou, Yang Shih-Neng, Zhang Geoffrey, Huang Tzung-Chi
Division of Pulmonary and Critical Care Medicine, Department of Internal Medicine, China Medical University Hospital, Taichung, Taiwan.
Department of Medical Imaging, Chang Bing Show Chwan Memorial Hospital, Changhua, Taiwan.
PLoS One. 2018 Feb 5;13(2):e0192002. doi: 10.1371/journal.pone.0192002. eCollection 2018.
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.
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.
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%.
The classification signature based on radiomics features demonstrated very good accuracy and high potential in clinical application.
放射组学可从诊断性医学图像中提取大量定量图像特征，已广泛应用于预后评估、治疗反应预测和癌症检测。肺结节的治疗方案取决于其诊断结果，即良性或恶性。传统上，肺结节诊断基于侵入性活检。最近，基于临床图像的非侵入性方法——放射组学特征，在病变分类、治疗结果预测方面显示出巨大潜力。
研究了基于计算机断层扫描（CT）图像数据的放射组学在肺结节分类中的应用，并引入了一种4特征标记用于肺结节分类。回顾性收集了72例患者的75个肺结节。对由经验丰富的放射肿瘤学家勾勒出轮廓的非增强CT图像进行放射组学特征提取。
在每个病例的750个图像特征中，发现76个特征在良性和恶性病变之间存在显著差异。一个放射组学标记由最佳的4个特征组成，包括Laws_LSL_min、Laws_SLL_能量、Laws_SSL_偏度和Laws_EEL_均匀度。使用该标记进行良性或恶性分类的准确率为84%，敏感性为92.85%，特异性为72.73%。
基于放射组学特征的分类标记在临床应用中显示出非常好的准确性和巨大潜力。
