Abstract

PurposeTo study the impact of abdominal deep inspiration breath hold (DIBH) technique on knowledge‐based radiotherapy treatment planning for left‐sided breast cancer to guide the application of DIBH technology.Materials and methodsTwo kernel density estimation (KDE) models were developed based on 40 left‐sided breast cancer patients with two CT acquisitions of free breathing (FB‐CT) and DIBH (DIBH‐CT). Each KDE model was used to predict dose volume histograms (DVHs) based on DIBH‐CT and FB‐CT for another 10 new patients similar to our training datasets. The predicted DVHs were taken as a substitute for dose constraints and objective functions in the Eclipse treatment planning system, with the same requirements for the planning target volume (PTV). The mean doses to the heart, the left anterior descending coronary artery (LADCA) and the ipsilateral lung were evaluated and compared using the T‐test among clinical plans, KDE predictions, and KDE plans.ResultsOur study demonstrated that the KDE model can generate deliverable simulations equivalent to clinically applicable plans. The T‐test was applied to test the consistency hypothesis on another ten left‐sided breast cancer patients. In cases of the same breathing status, there was no statistically significant difference between the predicted and the clinical plans for all clinically relevant DVH indices (P > 0.05), and all predicted DVHs can be transferred into deliverable plans. For DIBH‐CT images, significant differences were observed between FB model predictions and clinical plans (P < 0.05). DIBH model prediction cannot be optimized to a deliverable plan based on FB‐CT, with a counsel of perfection.ConclusionKDE models can predict DVHs well for the same breathing conditions but degrade with different breathing conditions. The benefits of DIBH for a given patient can be evaluated with a quick comparison of prediction results of the two models before treatment planning.

Highlights

  • deep inspiration breath hold (DIBH) model prediction cannot be optimized to a deliverable plan based on FB-CT, with a counsel of perfection

  • The results (Table2) show that there were no differences between clinical plans and kernel density estimation (KDE) predictions for both models in the training dataset, confirming that the dose volume histogram (DVH)

  • This research sought to reveal the impact of abdominal breath-holding on knowledge-based treatment planning for breast cancer radiotherapy so that we can guide the application of DIBH more precise before treatment

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Summary

Methods

Two kernel density estimation (KDE) models were developed based on 40 left-sided breast cancer patients with two CT acquisitions of free breathing (FB-CT) and DIBH (DIBH-CT). DVHs based on DIBH-CT and FB-CT for another 10 new patients similar to our training datasets. The predicted DVHs were taken as a substitute to dose constraints and objective functions in the Eclipse treatment planning system, with the same requirements for the planning target volume (PTV). Two KBMs (FB model and DIBH model) were built from 40 existing clinical plans that have ideal tumor coverage and OAR doses with different breath settings. Another 10 new patients were selected to investigate the performance of the prediction model.

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