Radiomic features analysis in computed tomography images of lung nodule classification.
Radiomic features analysis in computed tomography images of lung nodule classification.
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DOI：
10.1371/journal.pone.0192002
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发表时间：
2018
期刊：
影响因子：
3.7
通讯作者：
Huang TC
中科院分区：
文献类型：
--
作者：
Chen CH;Chang CK;Tu CY;Liao WC;Wu BR;Chou KT;Chiou YR;Yang SN;Zhang G;Huang TC
放射组学是从诊断医学图像中提取大量量化图像特征的方法，已被广泛应用于预测预后、治疗反应预测和癌症检测。肺结节的治疗选择取决于其诊断，无论是良性还是恶性。传统上，肺结节的诊断是基于侵入性活检。近年来，放射组学特征作为一种基于临床影像的非侵入性方法，在病变分类、治疗结果预测等方面显示出很大的潜力。研究了基于计算机断层扫描(CT)图像数据的放射组学肺结节分类方法，并将四特征特征用于肺结节分类。回顾性分析72例患者75个肺结节的临床资料。放射组学特征提取是在非增强CT图像上进行的，轮廓由经验丰富的放射肿瘤学家描绘。在每个病例的750个图像特征中，有76个特征在良、恶性病变之间存在显著差异。一个放射组学特征由最好的4个特征组成，包括Laws_LSL_Min、Laws_SLL_Energy、Laws_SSl_Skewness和Laws_Eel_Uniform。本征象用于良恶性鉴别诊断的准确率为84%，敏感性为92.85%，特异性为72.73%。基于放射组学特征的分类特征表现出很好的准确性和临床应用潜力。
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.
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影响因子：
5
作者：
Oliver JA;Budzevich M;Zhang GG;Dilling TJ;Latifi K;Moros EG
通讯作者：
Moros EG
DOI：
10.1016/j.radonc.2014.04.012
发表时间：
2014-07
期刊：
Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology
影响因子：
--
作者：
Oberije C;Nalbantov G;Dekker A;Boersma L;Borger J;Reymen B;van Baardwijk A;Wanders R;De Ruysscher D;Steyerberg E;Dingemans AM;Lambin P
通讯作者：
Lambin P
影响因子：
8.4
作者：
Eisenhauer, E. A.;Therasse, P.;Verweij, J.
通讯作者：
Verweij, J.
影响因子：
19.7
作者：
Li, F;Sone, S;Doi, K
通讯作者：
Doi, K
影响因子：
120.7
作者：
Bach, Peter B.;Mirkin, Joshua N.;Oliver, Thomas K.;Azzoli, Christopher G.;Berry, Donald A.;Brawley, Otis W.;Byers, Tim;Colditz, Graham A.;Gould, Michael K.;Jett, James R.;Sabichi, Anita L.;Smith-Bindman, Rebecca;Wood, Douglas E.;Qaseem, Amir;Detterbeck, Frank C.
通讯作者：
Detterbeck, Frank C.