AUTHOR=Hu Jiang , Liu Boji , Xie Weihao , Zhu Jinhan , Yu Xiaoli , Gu Huikuan , Wang Mingli , Wang Yixuan , Qi ZhenYu 

TITLE=Quantitative Comparison of Knowledge-Based and Manual Intensity Modulated Radiation Therapy Planning for Nasopharyngeal Carcinoma

JOURNAL=Frontiers in Oncology

VOLUME=Volume 10 - 2020

YEAR=2021

URL=https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2020.551763

DOI=10.3389/fonc.2020.551763

ISSN=2234-943X

ABSTRACT=Background and purpose:To validate the feasibility and efficiency of a fully automatic knowledge-based planning (KBP)method for nasopharyngeal carcinoma(NPC) cases, with special attention to the possible way that can improve the success rate of auto-planning. 
Methods and Materials: A knowledge-based dose volume histogram (DVH) prediction model was developed based on 99 formerly treated NPC patients, by means of which the optimization objectives and the corresponding priorities for intensity modulation radiation therapy (IMRT) planning were automatically generated for each head & neck organ at risk (OAR). The automatic KBP method was thus evaluated in 17 new NPC cases with comparison to manual plans (MP) and expert plans (EXP) in terms of target dose coverage, conformity index (CI), homogeneity index (HI) and normal tissue protection. To quantify the plan quality, a metric was applied for plan evaluation. The variation of plan quality and time consumption among planners were also investigated.
Results: With comparable target dose distributions, the KBP method achieved significant dose reduction in critical organs such as optic chiasm (p<0.001), optic nerve (p=0.021) and temporal lobe (p<0.001), but failed to spare the spinal cord (p<0.001) compared with MPs and EXPs. The overall plan quality evaluation gave the mean scores of 144.59±11.48, 142.71±15.18 and 144.82±15.17, respectively, for KBPs, MPs and EXPs (p=0.259). 15 out of 17 KBPs (i.e., 88.24%) were approved by our physician as clinical acceptable. 
Conclusion: The automatic KBP method using the DVH prediction model provides a possible way to generate clinically acceptable plans in a short time for NPC patients.