Predicting patient body weight and volumes changes using machine learning for head and neck adaptive radiotherapy
Keywords:
Adaptive radiation therapy (ART), Head and neck cancer (HNC), Machine learning (ML), Patient anatomical changeAbstract
Background: Patient anatomical change is the most challenging aspect of head and neck cancer (HNC) patients treated with adaptive radiotherapy (ART) techniques. These can lead to dosimetric deviations and result in an increase in severe radiation toxicity.
Objectives: To use machine learning (ML) in predicting anatomical changes and indicating the optimal time to modify plans in HNC patients for decision-making.
Materials and methods: Volumes of interest (VOI) of 183 Cone-beam computed tomography (CBCT) image datasets were defined based on planning Computed tomography (CT) images. The percentage of body weight (BW), Gross target volume (GTV), Clinical target volume (CTV)70, CTV59.4, Planning target volume (PTV)70, PTV59.4, left parotid gland (PG), and right PG volume changes were retrieved. 143 datasets (78.14%) were applied for training with various ML algorithms; support vector machines (SVMs), kernel approximation regression (KAR), gaussian process regression, Ensembles of trees (ETs), linear regression (LR), regression trees (RTs), and neural networks (NNs). Five and ten-fold cross-validation techniques were applied to select the best prediction model for each specific target. The selected model accuracy for a specific target was tested using the blind testing data set (40 plans or 21.86%) using root mean square error (RMSE) and R-square value (R2).
Results: The selected prediction model (k-fold cross validation, RMSE and R2) were RTs (5-fold, 4.10, 0.49), SVMs (5-fold, 7.40, 0.26), SVMs (5-fold, 5.74, 0.27), GPR (5-fold, 4.88, 0.13), SVMs (5-fold, 5.30, -0.04), ETs (5-fold, 3.19, 0.37), ETs (5-fold, 11.56, 0.57), and RTs (5-fold, 8.31, 0.7), respectively. The system generated the predicted data after the first 11th fractions.
Conclusion: This ML-based patient anatomical change model can provide useful information, which could benefit decision-making in treatment plan modification for HN-ART.
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