Literature DB >> 30302507

A density assignment method for dose monitoring in head-and-neck radiotherapy.

A Barateau1, N Perichon2, J Castelli2, U Schick3, O Henry2, E Chajon2, A Simon2, C Lafond2, R De Crevoisier2.   

Abstract

BACKGROUND AND
PURPOSE: During head-and-neck (H&N) radiotherapy, the parotid glands (PGs) may be overdosed; thus, a tool is required to monitor the delivered dose. This study aimed to assess the dose accuracy of a patient-specific density assignment method (DAM) for dose calculation to monitor the dose to PGs during treatment. PATIENTS AND METHODS: Forty patients with H&N cancer received an intensity modulated radiation therapy (IMRT), among whom 15 had weekly CTs. Dose distributions were calculated either on the CTs (CTref), on one-class CTs (1C-CT, water), or on three-class CTs (3C-CT, water-air-bone). The inter- and intra-patient DAM uncertainties were evaluated by the difference between doses calculated on CTref and 1C-CTs or 3C-CTs. PG mean dose (Dmean) and spinal cord maximum dose (D2%) were considered. The cumulated dose to the PGs was estimated by the mean Dmean of the weekly CTs.
RESULTS: The mean (maximum) inter-patient DAM dose uncertainties for the PGs (in cGy) were 23 (75) using 1C-CTs and 12 (50) using 3C-CTs (p ≤ 0.001). For the spinal cord, these uncertainties were 118 (245) and 15 (67; p ≤ 0.001). The mean (maximum) DAM dose uncertainty between cumulated doses calculated on CTs and 3C-CTs was 7 cGy (45 cGy) for the PGs. Considering the difference between the planned and cumulated doses, 53% of the ipsilateral and 80% of the contralateral PGs were overdosed by +3.6 Gy (up to 8.2 Gy) and +1.9 Gy (up to 5.2 Gy), respectively.
CONCLUSION: The uncertainty of the three-class DAM appears to be clinically non-significant (<0.5 Gy) compared with the PG overdose (up to 8.2 Gy). This DAM could therefore be used to monitor PG doses and trigger replanning.

Entities:  

Keywords:  Density assignment; Dose calculation; Dose-guided radiotherapy; Head-and-neck cancer; Parotid gland monitoring

Mesh:

Year:  2018        PMID: 30302507     DOI: 10.1007/s00066-018-1379-y

Source DB:  PubMed          Journal:  Strahlenther Onkol        ISSN: 0179-7158            Impact factor:   3.621


  1 in total

1.  A Deep Unsupervised Learning Model for Artifact Correction of Pelvis Cone-Beam CT.

Authors:  Guoya Dong; Chenglong Zhang; Xiaokun Liang; Lei Deng; Yulin Zhu; Xuanyu Zhu; Xuanru Zhou; Liming Song; Xiang Zhao; Yaoqin Xie
Journal:  Front Oncol       Date:  2021-07-16       Impact factor: 6.244

  1 in total

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