Predictive modeling of dose-volume parameters of carcinoma tongue cases using machine learning models

The aim of this study is to create a single institution-based machine learning model for a dose prediction generation tool for post-operative carcinoma of the tongue cases prospectively. Intensity-modulated radiotherapy (IMRT) plans for 20 patients with carcinoma of the tongue were generated using the Eclipse treatment planning system. A machine learning model was generated using a Python 3.10 computer language in a Jupyter notebook using Anaconda software. The PTVs and OARs doses obtained from the clinical treatment plans were used as a primary dataset. Machine learning models are built with two different datasets (10 and 20) for each selected volume. Volumes from 10 new sets of patients were fed into the software for predicting the corresponding dose values. Through the input given, the plan generated dose values of 10 patients were compared with the predicted outcomes of the 10 and 20 dataset models. The model created using the PTVs volume data predicted the dose values with increased accuracy. By verifying the model prediction with the TPS generated value, both the 10 and 20 dataset models predict all the 10 PTVs data within an error bound of 3% and most of the OARs data within an error bound of 5%. The dosimetric features implemented in the machine learning models reasonably predict both the PTVs dose parameter and OARs constraints and give confidence in decision-making during the clinical planning process.