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Updated: May 1, 2026
Voluntary Breath-hold Technique for Reducing Heart Dose in Left Breast Radiotherapy
Published on: July 3, 2014
An upfront patient selection strategy based on personalized data-driven computed tomography generation for deep
Yunxiang Wang1, Yihang Zhang2, Si-Ye Chen1
1Department of Radiation Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China.
Background:
Currently there is no widely used upfront selection method to determine whether patients are suitable for deep inspiration breath-hold (DIBH) in left-sided breast radiotherapy.
Purpose:
To establish an upfront patient selection strategy to improve the decision-making efficiency of DIBH and avoid extra computed tomography (CT) exposure to patients.
Methods:
A total of 174 patients who underwent both free-breathing (FB) and DIBH scans were enrolled. A general principal component analysis model for DIBH-CT synthesis was trained and consists of principal component feature vectors extracted from paired FB-CT and DIBH-CT in training set. The coefficients of the vectors were optimized to minimize the difference between synthetic CT and breath-hold scout image of each patient in test set, leading to personalized DIBH-CT synthesis. An upfront patient selection strategy was established based on cardiac dose in synthetic DIBH-CT plan. The performance of DIBH-CT synthesis was analyzed in terms of geometric and dosimetric consistency between synthetic and scanned DIBH-CTs. The accuracy of the patient selection strategy was evaluated. Time assumption of the patient selection workflow was analyzed.
Results:
Synthetic DIBH-CTs had average Dice similarity coefficients of 0.84 for the heart and 0.91 for the lungs compared with scanned DIBH-CTs. Synthetic DIBH-CT plans revealed an average mean heart dose reduction of 1.46 Gy, which was not significantly different from 1.51 Gy in scanned DIBH-CT plans (p = 0.878). The patient selection strategy yielded the correct benefit results with accuracy of 86.7 %. The average time assumption for patient selection was 11.9 ± 3.6 min.
Conclusions:
The proposed patient selection strategy can accurately identify patients benefiting from DIBH and provides a more efficient workflow for DIBH.
