# Computer Tools to Analyze Lung CT Changes after Radiotherapy
## 1 Introduction  
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. As the five year survival rate hardly reaches 20% even in well-developed countries, screening, diagnostic, and treatment improvement is needed. The use of IT tools in lung cancer diagnostics has been an important research topic for many years [1]. With growing computational power and artificial intelligence (AI) solutions, developing large-scale screening programs using computed tomography (CT) is possible. Early detection of lung cancer increases patients' chances to obtain an effective, radical treatment like surgery, systemic chemo- and immuno-therapy, or radiotherapy (RT). Radiation oncology itself uses sophisticated IT technologies to plan and deliver the treatment most safely and assess the early and late toxicity. Radical radiotherapy is an essential part of lung cancer treatment in all patients not eligible for radical surgery because of disease extent or medical comorbidities. It is also a necessary post-surgical adjuvant in some cases (positive surgical margins or lymph nodes) and plays a crucial role in palliative treatment. Despite the clinical scenario, the aim is to deliver a high radiation dose to the tumor cells, sparing normal tissue nearby (so-called OARs (organs at risk). Modern delivery techniques like IMRT (intensity modulated radiation therapy), VMAT (volumetric arc technique), or SBRT (stereotactic body radiotherapy), sophisticated particle modalities (protons or heavy-ions), and positioning accuracy (cone beam-CT imaging, respiratory motion gating) let us precisely conform the radiation beam and meet the OARs' dose constraints' criteria. However, there is still an issue of late and acute toxicity, especially concerning the lung tissue. Ten to 30% of patients develop asubacute radiation-induced pneumomitis (RIP) observed within six months post-treatment or radiation-induced lung fibrosis (RILF) as late toxicity (6-12 months after conventional RT) [2,3]. In clinical practice, 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]. Some other semi-quantitative fibrosis scoring methods like the Warrick score [5] are commonly used in connective tissue diseases. Nevertheless, 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]. However, in the era of modern dynamic RT delivery methods, it is important to focus on the impact of the lowest doses (0-5 Gy), which is not clear yet. 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' CT images after radiotherapy. The future clinical application is to observe subtle tissue density changes (represented by Hounsfield unit values) in anatomically corresponding parts of lungs in time. Our elaborated system supports the analysis of the association between the tissue changes and the dose delivered to the patient and other dosimetric and clinical factors. The analysis of many real patient cases can help define more precise dose and dose-volume constraints for future RT to prevent affecting lung morphology and function. Initially, the proposed tool was dedicated to analyzing the RILF only. However, currently, during the COVID-19 pandemic, the system can be used to measure post-COVID-19 pulmonary fibrosis. The paper is organized in the following way. After the Introduction, the next section presents the general structure of the proposed system.