Radiomics-based features for pattern recognition of lung cancer histopathology and metastases
Radiomics-based features for pattern recognition of lung cancer histopathology and metastases
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DOI：
10.1016/j.cmpb.2018.02.015
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发表时间：
2018-06-01
影响因子：
6.1
通讯作者：
de Azevedo-Marques, Paulo Mazzoncini
中科院分区：
文献类型：
--
作者：
Ferreira Junior, Jose Raniery;Koenigkam-Santos, Marcel;de Azevedo-Marques, Paulo Mazzoncini
背景和目的：肺癌是世界上与癌症相关的死亡的主要原因，其预后不良因肿瘤分期而异。计算机断层扫描(CT)是肺癌诊断和临床分期的首选影像方法。除肿瘤分期外，其他特征，如组织病理亚型，也可以增加预后信息。在这项工作中，基于放射组学的CT特征被用来通过机器学习模型来预测肺癌的组织病理学和转移。方法：对确诊的肺原发恶性肿瘤的局部影像数据进行回顾性评估，以进行检验和验证。采用相同方案采集的CT图像进行半自动分割。肿瘤的临床特征和计算机属性包括强度、直方图、纹理、形状和体积。三个机器学习分类器使用了多达100个选定的特征来执行分析。结果：基于放射组学的特征在受试者操作特征曲线下的面积在测试时分别为0.89、0.97和0.92，在验证淋巴结转移、远处转移和组织病理学模式识别时分别为0.75、0.71和0.81。结论：放射组学表征方法在计算模型中显示出巨大的潜力，可用于辅助肺癌组织病理亚型诊断的“虚拟活检”和用于治疗决策支持的转移预测，而不需要全身成像扫描。(C)2018爱思唯尔B.V.保留所有权利。
Background and Objectives: lung cancer is the leading cause of cancer-related deaths in the world, and its poor prognosis varies markedly according to tumor staging. Computed tomography (CT) is the imaging modality of choice for lung cancer evaluation, being used for diagnosis and clinical staging. Besides tumor stage, other features, like histopathological subtype, can also add prognostic information. In this work, radiomics-based CT features were used to predict lung cancer histopathology and metastases using machine learning models. Methods: local image datasets of confirmed primary malignant pulmonary tumors were retrospectively evaluated for testing and validation. CT images acquired with same protocol were semiautomatically segmented. Tumors were characterized by clinical features and computer attributes of intensity, histogram, texture, shape, and volume. Three machine learning classifiers used up to 100 selected features to perform the analysis. Results: radiomics-based features yielded areas under the receiver operating characteristic curve of 0.89, 0.97, and 0.92 at testing and 0.75, 0.71, and 0.81 at validation for lymph nodal metastasis, distant metastasis, and histopathology pattern recognition, respectively. Conclusions: the radiomics characterization approach presented great potential to be used in a computational model to aid lung cancer histopathological subtype diagnosis as a "virtual biopsy" and metastatic prediction for therapy decision support without the necessity of a whole-body imaging scanning. (c) 2018 Elsevier B.V. All rights reserved.