Diaphragm and abdominal organ motion during radiotherapy: a comprehensive multicenter study in 189 children

Explicitly encoding the cyclic nature of breathing signal allows for accurate breathing motion prediction in radiotherapy with minimal training data

Background and purpose

Active breathing motion management in radiotherapy consists of motion monitoring, quantification and mitigation. It is impacted by associated latencies of a few 100 ms. Artificial neural networks can successfully predict breathing motion and eliminate latencies. However, they require usually a large dataset for training. The objective of this work was to demonstrate that explicitly encoding the cyclic nature of the breathing signal into the training data enables significant reduction of training datasets which can be obtained from healthy volunteers.

Material and methods

Seventy surface scanner breathing signals from 25 healthy volunteers in anterior-posterior direction were used for training and validation (ratio 4:1) of long short-term memory models. The model performance was compared to a model using decomposition into phase, amplitude and a time-dependent baseline. Testing of the models was performed on 55 independent breathing signals in anterior-posterior direction from surface scanner (35 lung, 20 liver) of 30 patients with a mean breathing amplitude of (5.9 ± 6.7) mm.

Results

Using the decomposed breathing signal allowed for a reduction of the absolute root-mean square error (RMSE) from 0.34 mm to 0.12 mm during validation. Testing using patient data yielded an average absolute RMSE of the breathing signal of (0.16 ± 0.11) mm with a prediction horizon of 500 ms.

Conclusion

It was demonstrated that a motion prediction model can be trained with less than 100 datasets of healthy volunteers if breathing cycle parameters are considered. Applied to 55 patients, the model predicted breathing motion with a high accuracy.

Paediatric radiation therapy without anaesthesia - Are the children moving?

PURPOSE

Children who require radiation therapy (RT) should ideally be treated awake, without anaesthesia, if possible. Audiovisual distraction is a known method to facilitate awake treatment, but its effectiveness at keeping children from moving during treatment is not known. The aim of this study was to evaluate intrafraction movement of children receiving RT while awake.

METHODS

In this prospective study, we measured the intrafraction movement of children undergoing treatment with fractionated RT, using pre- and post-RT cone beam CT (CBCT) with image matching on bony anatomy. Study CBCTs were acquired at first fraction, weekly during RT, and at last fraction. The primary endpoint was the magnitude of vector change between the pre- and post-RT scans. Our hypothesis was that 90 % of CBCT acquisitions would have minimal movement, defined as <3 mm for head-and-neck (HN) treatments and <5 mm for non-HN treatments.

RESULTS

A total of 65 children were enrolled and had evaluable data across 302 treatments with CBCT acquisitions. Median age was 11 years (range, 2-18; 1st and 3rd quartiles 7 and 14 years, respectively). Minimal movement was observed in 99.4 % of HN treatments and 97.2 % of non-HN treatments. The study hypothesis of >90 % of evaluations having minimal movement was met. Children who were age >11 years moved less at initial evaluation but tended to move more as a course of radiation progressed, as compared to children who were younger.

CONCLUSION

Children receiving RT with audiovisual distraction while awake had small magnitudes of observed intrafraction movement, with minimal movement in >97 % of observed RT fractions. This study validates methods of anaesthesia avoidance using audiovisual distraction for selected children.