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Most cancer patients experience severe pain at some point during their illness. Unfortunately, this pain is often underestimated and poorly managed by healthcare professionals until it becomes very intense. This inadequate pain management can negatively impact both the physical and mental well-being of patients. Identifying cancer pain early on is difficult but crucial for preventing severe discomfort. This research aims to detect pain early by analyzing medical images of patients. To develop an algorithm for this purpose, we will use two computer science techniques: one to gather data about pain from medical records, and another to extract information from medical images. We will use the first technique to quantify the pain levels documented in patients’ medical notes and the second technique to analyze radiographic images to assess tumour characteristics, such as volume and shape. We will then validate our model using pain scores collected from future cancer patients through a mobile app created by our team (). A key innovation of our model will be its ability to predict pain from radiographic images before it actually occurs. We aim to accomplish this feat by expanding upon the research previously conducted by Dr. Hossein Naseri, in that we will incorporate delta-radiomic features obtained from cone-beam CT data into a model that previously only utilized radiomic features from CT data.