Cone beam CT verification for active breathing control (ABC)-gated radiotherapy for lung cancer

Correlation of Optical Surface Respiratory Motion Signal and Internal Lung and Liver Tumor Motion: A Retrospective Single-Center Observational Study

Purpose: Surface-guided radiation therapy (SGRT) application has limitations. This study aimed to explore the relationship between patient characteristics and their external/internal correlation to qualitatively assess the external/internal correlation in a particular patient. Methods: Liver and lung cancer patients treated with radiotherapy in our institution were retrospectively analyzed. The external/internal correlation were calculated with Spearman correlation coefficient (SCC) and SCC after support vector regression (SVR) fitting (SCC_svr). The relationship between the external/internal correlation and magnitudes of motion of the tumor and external marker (A_i, A_e), tumor volume V_t, patient age, gender, and tumor location were explored. Results: The external/internal motions of liver and lung cancer patients were strongly correlated in the S-I direction, with mean SCC_svr values of 0.913 and 0.813. The correlation coefficients between the external/internal correlations and the patients’ characteristics (A_i, A_e, V_t, and age) were all smaller than 0.5; A_i, A_e and liver tumor volumes were positively correlated with the strength of the external/internal correlation, while lung tumor volumes and patient age were negative. The external/internal correlations in males and females were roughly equal, and the external/internal correlations in patients with peripheral lung cancers were stronger than those in patients with central lung cancers. Conclusion: The external/internal correlation shows great individual differences. The effects of A_i, A_e, V_t, and age are weakly to moderately correlated. Our results suggest the necessity of individualized assessment of patient's external/internal motion correlation prior to the application of SGRT technique for breath motion monitoring.

Real-time liver tracking algorithm based on LSTM and SVR networks for use in surface-guided radiation therapy

Background

Surface-guided radiation therapy can be used to continuously monitor a patient’s surface motions during radiotherapy by a non-irradiating, noninvasive optical surface imaging technique. In this study, machine learning methods were applied to predict external respiratory motion signals and predict internal liver motion in this therapeutic context.

Methods

Seven groups of interrelated external/internal respiratory liver motion samples lasting from 5 to 6 min collected simultaneously were used as a dataset, D_v. Long short-term memory (LSTM) and support vector regression (SVR) networks were then used to establish external respiratory signal prediction models (LSTMpred/SVRpred) and external/internal respiratory motion correlation models (LSTMcorr/SVRcorr). These external prediction and external/internal correlation models were then combined into an integrated model. Finally, the LSTMcorr model was used to perform five groups of model updating experiments to confirm the necessity of continuously updating the external/internal correlation model. The root-mean-square error (RMSE), mean absolute error (MAE), and maximum absolute error (MAX_AE) were used to evaluate the performance of each model.

Results

The models established using the LSTM neural network performed better than those established using the SVR network in the tasks of predicting external respiratory signals for latency-compensation (RMSE < 0.5 mm at a latency of 450 ms) and predicting internal liver motion using external signals (RMSE < 0.6 mm). The prediction errors of the integrated model (RMSE ≤ 1.0 mm) were slightly higher than those of the external prediction and external/internal correlation models. The RMSE/MAE of the fifth model update was approximately ten times smaller than that of the first model update.

Conclusions

The LSTM networks outperform SVR networks at predicting external respiratory signals and internal liver motion because of LSTM’s strong ability to deal with time-dependencies. The LSTM-based integrated model performs well at predicting liver motion from external respiratory signals with system latencies of up to 450 ms. It is necessary to update the external/internal correlation model continuously.

Deep inspiration breath-hold radiotherapy for lung cancer: impact on image quality and registration uncertainty in cone beam CT image guidance

OBJECTIVE

We investigated the impact of deep inspiration breath-hold (DIBH) and tumour baseline shifts on image quality and registration uncertainty in image-guided DIBH radiotherapy (RT) for locally advanced lung cancer.

METHODS

Patients treated with daily cone beam CT (CBCT)-guided free-breathing (FB) RT had an additional CBCT in DIBH at three fractions. These CBCT scans were offline rigidly registered (on tumour) to FB and DIBH CT scans acquired at planning. All registrations were repeated to evaluate the intraobserver uncertainty. CBCT scans were scored on degree of streak artefacts and visualization of tumour and anatomical structures. We examined the impact of tumour baseline shift between consecutive DIBHs on CBCT image quality.

RESULTS

CBCT scans from 15 patients were analysed. Intraobserver image registration uncertainty was approximately 2 mm in both FB and DIBH, except for the craniocaudal direction in FB, where it was >3 mm. On the 31st fraction, the intraobserver uncertainty increased compared with the second fraction. This increase was more pronounced in FB. Image quality scores improved in DIBH compared with FB for all parameters in all patients. Simulated tumour baseline shifts ≤2 mm did not affect the CBCT image quality considerably.

CONCLUSION

DIBH CBCT improved image quality and reduced registration uncertainty in the craniocaudal direction in image-guided RT of locally advanced lung cancer. Baseline shifts ≤2 mm in DIBH during CBCT acquisition did not affect image quality. Advances in knowledge: DIBH RT has dosimetric advantages over FB; this work demonstrates an additional benefit of DIBH in terms of registration accuracy because of improved image quality.