Automated planning of whole breast irradiation using hybrid IMRT improves efficiency and quality

采用混合式调强放射治疗(IMRT)进行全乳照射的自动化计划可提高效率和质量。

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Abstract

PURPOSE: To develop an automated workflow for whole breast irradiation treatment planning using hybrid intensity modulated radiation therapy (IMRT) approach and to demonstrate that this workflow can improve planning quality and efficiency when compared to manual planning. METHODS: The auto planning framework was built based on scripting with MIM and Pinnacle systems. MIM workflows were developed to automatically segment normal structures and targets, identify landmarks for beam placement, select beam energies, and set beam configurations. Pinnacle scripts were generated from the MIM workflow to create hybrid IMRT plans automatically. Each hybrid IMRT plan included two prescriptions: a three-dimensional (3D) prescription consisted of two open tangent beams, and an IMRT prescription consisted of two step-and-shoot IMRT beams. The 3D prescription delivered a full prescription dose to the maximum dose point, and the IMRT prescription was optimized to deliver a uniform dose to the entire breast while sparing dose to the normal structures. For 30 patients, the auto plans were compared with clinically accepted manual plans using the paired sample t-test. RESULTS: The auto planning process took approximately 8 min to complete. The mean dice coefficients between auto-segmentation and manual contours were 0.98, 0.94 and 0.88 for the lungs, heart, and PTVeval_Breast, respectively. The MUs of the auto plans was on average 13% higher than that of the manual plans. Auto planning improved plan quality significantly: percentage volume receiving 95% of the prescription dose (V95%) of the PTVeval_Breast increased from 91.5% to 93.2% (P = 0.001), V105% of the PTVeval_Breast decreased from 7.2% to 1.2% (P = 0.013), V20Gy of the ipsilateral lung decreased from 13.1% to 10.4% (P = 0.001) and mean heart dose for left-sided breast patients decreased from 1.2 Gy to 0.9 Gy (P < 0.001). CONCLUSION: An automated treatment planning process can make the planning process efficient with improved plan quality.

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