Abstract
Background: Radiomics, as the process of acquiring and analysing quantitative imaging characteristics, is the novel method which has been actively investigated in oncology for its ability to estimate treatment response and patients’ survival based on the CT, MRI and PET imaging.
Aim: Therefore, the aim of this study is to understand the use and significance of radiomics in forecasting treatment responses and the course of oncological diseases in the context of modern oncology’s weaknesses in cancer individualization, as well as the importance of proper prediction.
Method: A specific strategy was utilizing a retrospective cohort study, that involved the assessment of radiomic features based on medical images and patients’ information from their records. It was used statistical and machine learning approaches to extract and use radiomic features, and to build prediction models. In this study ethical considerations were considered.
Results: Participants’ demographic data, cancer diagnosis, and extent were considered. The important aspects for explaining the treatment responder population were delineated, and performances for several statistical models were confirmed and compared across different cohorts. Survivals and recurrences where other patients did or did not experience were considered, along with major subgroups to identify that the predictions’ accuracy regarding the occurrence of cancer were based on the type, stage, and treatment plan.
Conclusion: In oncology, the application of radiomics becomes evident and important as it defines the role to predict the response of the treatment as well as patient prognosis. Its integration can be expected to help in progressing cancer treatment methods and the best practices for patients’ management and therefore meaningfully impact the field of oncology.
Keywords: Radiomics, Oncology, Treatment Response, Patient Outcomes, Predictive Modelling, Medical Imaging, Machine Learning, Personalized Medicine.