The benefits evaluation of abdominal deep inspiration breath hold based on knowledge‐based radiotherapy treatment planning for left‐sided breast cancer

Abstract

Purpose To 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 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). 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. Results Our 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. Conclusion KDE 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.

A Critical Overview of Predictors of Heart Sparing by Deep-Inspiration-Breath-Hold Irradiation in Left-Sided Breast Cancer Patients

“…The prediction model drawn up by Xu et al [ 42 ] was developed from a kernel density estimation of dose-volume histograms (DVHs) of FB- and DIBH-IMRT plans by extracting features from a reference training dataset according to a knowledge-based approach (machine learning-like). The DVH prediction worked only within the same breath setting: the FB and DIBH models were able to predict the FB and DIBH clinical plan dosimetries, respectively, but each model was unable to predict the reverse respiratory condition (FB → DIBH and vice versa), hence the need for two CT scans in any case.…”

Skin Surface Dose for Whole Breast Radiotherapy Using Personalized Breast Holder: Comparison with Various Radiotherapy Techniques and Clinical Experiences

“…Furthermore, each increase in the mean dose to the heart by 1 Gy increases the risk of cardiotoxicity by four percent [8]. There are two common heart-sparing radiotherapy techniques, and the first is deep inspiration breath hold (DIBH) [9][10][11][12][13][14][15][16]. DIBH can increase the distance from the heart to the treatment field due to lung expansion, resulting in a significant reduction in the mean and maximum doses to the heart and left ventricle [17].…”