Automatic Staging of Cancer Tumors Using AIM Image Annotations and Ontologies.
Automatic Staging of Cancer Tumors Using AIM Image Annotations and Ontologies.
复制标题
使用 AIM 图像注释和本体对癌症肿瘤进行自动分期。
DOI：
10.1007/s10278-019-00251-x
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发表时间：
2020
影响因子：
4.4
通讯作者：
Moreira,DA
中科院分区：
文献类型：
--
作者：
Luque,EF;Miranda,N;Rubin,DL;Moreira,DA
当临床医生评估患者的治疗进展时，关于癌症分期的第二种意见是至关重要的。分期是一个考虑患者肿瘤的描述、位置、特征和可能的转移的过程。它应该遵循标准，如恶性肿瘤的TNM分类。然而，在临床实践中，这一过程的实施可能是乏味和容易出错的。为了缓解这些问题，我们打算通过在癌症分期评估中提供第二种意见来帮助放射科医生。为此，我们开发了一个基于语义注释的TNM分类器，该分类器由放射科医生使用ePad工具制作。它使用公理和规则将注释(以AIM格式存储)转换为AIM4-O本体实例。从那时起，它自动计算出肝脏TNM癌症分期。作为这项工作的一部分，开发了AIM4-O本体来表示Web本体语言(OWL)中的注释。我们使用了来自NCI基因组数据共享(GDC)的51份带有分期数据的肝脏放射学报告的数据集来评估我们的分类器。与医生分类比较，分类准确率为85.7%，召回率为81.0%。此外，来自2个不同机构的3名放射科医生手动审查了51份记录中4份的随机样本，并同意工具分期。AIM4-O也得到了较好的评价。我们的分类器可以集成到AIM感知成像工具中，例如ePad，以提供作为癌症治疗工作流程一部分的分期的第二意见。
A second opinion about cancer stage is crucial when clinicians assess patient treatment progress. Staging is a process that takes into account description, location, characteristics, and possible metastasis of tumors in a patient. It should follow standards, such as the TNM Classification of Malignant Tumors. However, in clinical practice, the implementation of this process can be tedious and error prone. In order to alleviate these problems, we intend to assist radiologists by providing a second opinion in the evaluation of cancer stage. For doing this, we developed a TNM classifier based on semantic annotations, made by radiologists, using the ePAD tool. It transforms the annotations (stored using the AIM format), using axioms and rules, into AIM4-O ontology instances. From then, it automatically calculates the liver TNM cancer stage. The AIM4-O ontology was developed, as part of this work, to represent annotations in the Web Ontology Language (OWL). A dataset of 51 liver radiology reports with staging data, from NCI’s Genomic Data Commons (GDC), were used to evaluate our classifier. When compared with the stages attributed by physicians, the classifier stages had a precision of 85.7% and recall of 81.0%. In addition, 3 radiologists from 2 different institutions manually reviewed a random sample of 4 of the 51 records and agreed with the tool staging. AIM4-O was also evaluated with good results. Our classifier can be integrated into AIM aware imaging tools, such as ePAD, to offer a second opinion about staging as part of the cancer treatment workflow.