Head and neck (HN) cancer patients undergoing radiotherapy may experience significant anatomical changes due to weight loss and tumor shrinkage. These changes can impact the effectiveness of the initial treatment plan, potentially necessitating treatment replanning. However, ad hoc replanning requires additional clinical staff time, which can lead to suboptimal and stressful treatment planning. Furthermore, currently, there is no established method for determining the total amount of anatomical variation in the head and neck region to decide whether replanning is necessary. This research aimed to identify and create metrics based on patient anatomical structures that can describe the anatomical alterations that patients may experience throughout the treatment and influence decisions regarding treatment replanning. These parameters were used to develop a machine learning classification model to predict if patients would likely undergo replanning. Based on the 3D shape and 2D contours of structures, we defined 43 parameters.
This repository contains the ideas, the tries, and the mistakes made during the research process, including the code for the graphs generation, data processing, and ML. Despite containing parts of the code, the Jupyter notebooks may not be well commented, and sometimes they may contain some spanglish!
Odette Rios-Ibacache
If you have any questions regarding the set of codes, please email me!
Contact email: [email protected]