Zarinabad Niloufar, Meeus Emma M, Manias Karen, Foster Katharine, Peet Andrew
Institute of Cancer and Genomic Sciences, University of Birmingham, Birmingham, United Kingdom.
Birmingham Children Hospital NHS Trust, Birmingham, United Kingdom.
JMIR Med Inform. 2018 May 2;6(2):e30. doi: 10.2196/medinform.9171.
Advances in magnetic resonance imaging and the introduction of clinical decision support systems has underlined the need for an analysis tool to extract and analyze relevant information from magnetic resonance imaging data to aid decision making, prevent errors, and enhance health care.
The aim of this study was to design and develop a modular medical image region of interest analysis tool and repository (MIROR) for automatic processing, classification, evaluation, and representation of advanced magnetic resonance imaging data.
The clinical decision support system was developed and evaluated for diffusion-weighted imaging of body tumors in children (cohort of 48 children, with 37 malignant and 11 benign tumors). Mevislab software and Python have been used for the development of MIROR. Regions of interests were drawn around benign and malignant body tumors on different diffusion parametric maps, and extracted information was used to discriminate the malignant tumors from benign tumors.
Using MIROR, the various histogram parameters derived for each tumor case when compared with the information in the repository provided additional information for tumor characterization and facilitated the discrimination between benign and malignant tumors. Clinical decision support system cross-validation showed high sensitivity and specificity in discriminating between these tumor groups using histogram parameters.
MIROR, as a diagnostic tool and repository, allowed the interpretation and analysis of magnetic resonance imaging images to be more accessible and comprehensive for clinicians. It aims to increase clinicians' skillset by introducing newer techniques and up-to-date findings to their repertoire and make information from previous cases available to aid decision making. The modular-based format of the tool allows integration of analyses that are not readily available clinically and streamlines the future developments.
磁共振成像技术的进步以及临床决策支持系统的引入凸显了一种分析工具的必要性，该工具能够从磁共振成像数据中提取并分析相关信息，以辅助决策、预防错误并改善医疗保健。
本研究旨在设计并开发一种模块化医学图像感兴趣区域分析工具及知识库（MIROR），用于对高级磁共振成像数据进行自动处理、分类、评估和呈现。
针对儿童身体肿瘤的扩散加权成像（48名儿童队列，其中37例为恶性肿瘤，11例为良性肿瘤）开发并评估了临床决策支持系统。使用Mevislab软件和Python开发了MIROR。在不同的扩散参数图上围绕良性和恶性身体肿瘤绘制感兴趣区域，并利用提取的信息区分恶性肿瘤和良性肿瘤。
使用MIROR时，与知识库中的信息相比，为每个肿瘤病例得出的各种直方图参数为肿瘤特征描述提供了额外信息，并有助于区分良性和恶性肿瘤。临床决策支持系统的交叉验证显示，使用直方图参数区分这些肿瘤组时具有较高的敏感性和特异性。
MIROR作为一种诊断工具和知识库，使临床医生对磁共振成像图像的解读和分析更加便捷和全面。它旨在通过将更新的技术和最新研究结果引入临床医生的技能库来提高他们的技能，并提供以前病例的信息以辅助决策。该工具基于模块化的格式允许整合临床上不易获得的分析，并简化未来的开发。
