Application of dose-volume histogram prediction in biologically related models for nasopharyngeal carcinomas treatment planning

Wufei Cao,Yongdong Zhuang,Xiaowei Liu,Lixin Chen

doi:10.1186/s13014-020-01623-2

Abstract

PurposeIn this study, we employed a gated recurrent unit (GRU)-based recurrent neural network (RNN) using dosimetric information induced by individual beam to predict the dose-volume histogram (DVH) and investigated the feasibility and usefulness of this method in biologically related models for nasopharyngeal carcinomas (NPC) treatment planning.Methods and materialsOne hundred patients with NPC undergoing volumetric modulated arc therapy (VMAT) between 2018 and 2019 were randomly selected for this study. All the VMAT plans were created using the Monaco treatment planning system (Elekta, Sweden) and clinically approved: > 98% of PGTVnx received the prescribed doses of 70 Gy, > 98% of PGTVnd received the prescribed doses of 66 Gy and > 98% of PCTV received 60 Gy. Of these, the data from 80 patients were used to train the GRU-RNN, and the data from the other 20 patients were used for testing. For each NPC patient, the DVHs of different organs at risk were predicted by a trained GRU-based RNN using the information given by individual conformal beams. Based on the predicted DVHs, the equivalent uniform doses (EUD) were calculated and applied as dose constraints during treatment planning optimization. The regenerated VMAT experimental plans (EPs) were evaluated by comparing them with the clinical plans (CPs).ResultsFor the 20 test patients, the regenerated EPs guided by the GRU-RNN predictive model achieved good consistency relative to the CPs. The EPs showed better consistency in PTV dose distribution and better dose sparing for many organs at risk, and significant differences were found in the maximum/mean doses to the brainstem, brainstem PRV, spinal cord, lenses, temporal lobes, parotid glands and larynx with P-values < 0.05. On average, compared with the CPs, the maximum/mean doses to these OARs were altered by − 3.44 Gy, − 1.94 Gy, − 1.88 Gy, 0.44 Gy, 1.98 Gy, − 1.82 Gy and 2.27 Gy, respectively. In addition, significant differences were also found in brainstem and spinal cord for the dose received by 1 cc volume with 4.11 and 1.67 Gy dose reduction in EPs on average.ConclusionThe GRU-RNN-based DVH prediction method was capable of accurate DVH prediction. The regenerated plans guided by the predicted EUDs were not inferior to the manual plans, had better consistency in PTVs and better dose sparing in critical OARs, indicating the usefulness and effectiveness of biologically related model in knowledge-based planning.

Highlights

Research background and purpose Intensity-modulated radiation therapy (IMRT) and volumetric modulated arc therapy (VMAT) allow increased conformity of high-radiation-dose regions to the planning target volume (PTV) while sparing each organ at risk (OAR) [1]
For the 20 test patients, the regenerated experimental plans (EPs) guided by the gated recurrent unit (GRU)-recurrent neural network (RNN) predictive model achieved good consistency relative to the clinical plans (CPs)
The gated recurrent unit-based recurrent neural network (GRU-RNN)-based dose-volume histogram (DVH) prediction method was capable of accurate DVH prediction

Summary

Introduction

Research background and purpose Intensity-modulated radiation therapy (IMRT) and volumetric modulated arc therapy (VMAT) allow increased conformity of high-radiation-dose regions to the planning target volume (PTV) while sparing each organ at risk (OAR) [1]. A number of efforts to aid in treatment planning using knowledge-based planning (KBP) techniques have improved the consistency of plan quality and reduced the required optimization time. Most of these efforts were developed based on establishing a correlation between the OAR-PTV anatomy and the OAR cumulative dose-volume histogram (DVH). One concern regarding the DTH and OVH is that their simplicity may lead to inaccurate presentation of the interpatient variations in anatomical features, which may have an impact on the organ dose deposition [5, 6], especially for complex tumour volumes in close proximity to critical structures such as those observed in nasopharyngeal carcinomas (NPCs). The dose deposited in an OAR voxel depends on its distance from the PTV surface and on the treatment beam orientation [5, 7, 8]

Methods

Results

Discussion

Conclusion