Prediction of plan adaptation in head and neck cancer proton therapy using clinical, radiographic, and dosimetric features

Abstract

Purpose

Because proton head and neck (HN) treatments are sensitive to anatomical changes, plan adaptation (re-plan) during the treatment course is needed for a significant portion of patients. We aim to predict re-plan at plan review stage for HN proton therapy with a neural network (NN) model trained with patients’ dosimetric and clinical features. The model can serve as a valuable tool for planners to assess the probability of needing to revise the current plan.

Methods and Materials

Mean beam dose heterogeneity index (BHI), defined as the ratio of the maximum beam dose to the prescription dose, plan robustness features (clinical target volume (CTV), V100 changes, and V100 > 95% passing rates in 21 robust evaluation scenarios), as well as clinical features (e.g., age, tumor site, and surgery/chemotherapy status) were gathered from 171 patients treated at our proton center in 2020, with a median age of 64 and stages from I-IVc across 13 HN sites. Statistical analyses of dosimetric parameters and clinical features were conducted between re-plan and no-replan groups. A NN was trained and tested using these features. Receiver operating characteristic (ROC) analysis was conducted to evaluate the performance of the prediction model. A sensitivity analysis was done to determine feature importance.

Results

Mean BHI in the re-plan group was significantly higher than the no-replan group (p < .01). Tumor site (p < .01), chemotherapy status (p < .01), and surgery status (p < .01) were significantly correlated to re-plan. The model had sensitivities/specificities of 75.0%/77.4%, respectively, and an area under the ROC curve of .855.

Conclusion

There are several dosimetric and clinical features that correlate to re-plans, and NNs trained with these features can be used to predict HN re-plans, which can be used to reduce re-plan rate by improving plan quality.

Keywords:

• Proton head neck therapy
• plan adaptation prediction
• neural networks
• beam dose heterogeneity

Acknowledgements

N/A.

Data sharing

Due to ethical/legal/commercial reasons, supporting data is not available.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability statement

Research data are not available at this time.