Abstract
The paper describes a computer tool dedicated to the comprehensive analysis of lung changes in computed tomography (CT) images. The correlation between the dose delivered during radiotherapy and pulmonary fibrosis is offered as an example analysis. The input data, in DICOM (Digital Imaging and Communications in Medicine) format, is provided from CT images and dose distribution models of patients. The CT images are processed using convolution neural networks, and next, the selected slices go through the segmentation and registration algorithms. The results of the analysis are visualized in graphical format and also in numerical parameters calculated based on the images analysis.
Highlights
Lung cancer remains one of the most critical oncology challenges, with more than 2.2 million new cases diagnosed and almost 1.8 million deaths in 2020
This study aims to develop a software tool that unifies the workflow of state-of-the-art solutions for an automatic, fast, large-scale radiomic comparison of lung cancer patients’ computed tomography (CT) images after radiotherapy
Mass-preserving—registration relies on detecting density changes on CT images, which are related to different inhalation volumes
Summary
Lung cancer remains one of the most critical oncology challenges, with more than 2.2 million new cases diagnosed and almost 1.8 million deaths in 2020. The assessment of RILF is based on various grading scales including the Common Terminology Criteria for Adverse Events (CTCAE), Radiation Therapy Oncology Group (RTOG) criteria, or LENT-SOMA(EORTC) scoring They mainly focus on clinical presentation, partially supported by imaging finding [4]. In oligosymptomatic patients, the presented scales do not meet radiation oncologists needs, and it seems necessary to use precise, numeric assessment of CT changes based on density values. This approach confirms the time evolution and the impact of mid- and high-radiation doses on RILF [6]. The last sections presents the conclusion and the possible application in COVID related problems
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