Abstract
Artificial Intelligence (AI) has been widely employed in the medical field in recent years in such areas as image segmentation, medical image registration, and computer-aided detection. This study explores one application of using AI in adaptive radiation therapy treatment planning by predicting the tumor volume reduction rate (TVRR). Cone beam computed tomography (CBCT) scans of twenty rectal cancer patients were collected to observe the change in tumor volume over the course of a standard five-week radiotherapy treatment. In addition to treatment volume, patient data including patient age, gender, weight, number of treatment fractions, and dose per fraction were also collected. Application of a stepwise regression model showed that age, dose per fraction and weight were the best predictors for tumor volume reduction rate.
Highlights
50% of all cancer patients are treated using radiotherapy, with approximately 40% of patients who receive curative treatment for cancer being within this figure [1]
An oncologist can look at approximately 2400 treatment planning cases in 10 years, while Artificial Intelligence (AI) can start with 2400 treatment planning cases to train itself and reach millions of cases within a concise period of time [6]
Five Cone Beam Computed Tomography (CBCT) Scans were obtained for each patient, one Cone beam computed tomography (CBCT) per week for the entire five weeks of the treatment course
Summary
50% of all cancer patients are treated using radiotherapy, with approximately 40% of patients who receive curative treatment for cancer being within this figure [1]. Technological advances have enabled clinicians to model the delivered radiation fields to the tumor shape and have led to advanced treatments, such as intensity modulated radiation therapy (IMRT) and volumetric modulated arc radiotherapy (VMAT) [3]. In this era, further software and technology developments allow rapid and consistent production of automated treatment planning [4]. There are many classifications of automated treatment planning, such as knowledge-based, expert-based, or AI based treatment planning [5]. By implementing the concept of AI to interrogate the tremendous amount of treatment data and medical images available at any hospital, one may enable the delivery of improved stratified or personalized treatment [10,11]
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