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Improved Normal Tissue Sparing in Nasopharyngeal Carcinoma Radiotherapy Using Biological Cost Function

W Bai1 , Y Gao2 , R Zhang1*, S Wang1 , Y Qiao1 , x Qiao1 , (1) The Fourth Hospital of Hebei Medical University, Shijiazhuang, Hebei,Chnia (2) Department of Gastroenterology, Hebei General Hospital, Shijiazhuang, China


(Sunday, 7/14/2019)  

Room: ePoster Forums

Purpose: Volumetric modulated arc therapy (VMAT) has reduced the impact of acute and late toxicities associated with head and neck radiotherapy. Treatment planning system advances in biological cost function optimization and improved segmentation techniques (Monaco v5.10) have improved organ at risk (OAR) sparing. A planning study was undertaken to quantify the potential benefits of Monaco biological optimization for OAR avoidance compared to the Oncentra Planning System (v4.1).

Methods: Simultaneous integrated boost treatment plans were produced for 11 patients with nasopharyngeal carcinoma using both planning systems which supportVMAT optimization. Plans were compared for target coverage and OAR avoidance. Comparisons were made using Monaco's Monte Carlo dose engine to eliminate differences due to inherent algorithms.

Results: All plans sufficiently conformed to the planning objectives and were clinically acceptable for both solutions. Monaco produced lower OAR doses, with significant improvement to left (10.2%, p =0.004) and right parotid mean dose (15.3%, p =0.003), Optic nerve V50Gy (3.0%, p=0.004), spinal cord (10.3%, p<0.001) ,brain stem and left,right lens dose maximums are decresed by 15.1%,13.3%,12.5%, respectively.

Conclusion: Monaco based planning with biological bost function delivers substantial improvement to OAR avoidance than Oncentra Planning System for all radically treated nasopharyngeal carcinoma patients. Further investigations should be undertaken to review any clinical benefit of this improved planned dosimetry.


Biological Dosimetry, Inverse Planning, Monte Carlo


TH- External beam- photons: dose computation engines- Monte Carlo

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