Response to “Comment on ‘A planning quality evaluation tool for prostate adaptive IMRT based on machine learning’ ” [Med. Phys. 38, 719 (2011)]

Zhu, Xiaofeng; Li, Taoran; Yin, Fang‐Fang; Wu, Qiuwen; Ge, Yaorong

doi:10.1118/1.3578613

Cited by 11 publications

(7 citation statements)

References 4 publications

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“…The dose deposited in an OAR voxel depends not only on its distance to PTV surface, but also on treatment beam orientations. 3,11,12 Two OAR voxels with the same distance to the PTV may have large difference in received dose, simply because one is in the beam's direction and the other is outside the radiation field. As examples, a prostate and a HN plan with calculated dose distributions are shown in Figs.…”

Section: Iib Factors That Affect Oar Dosesmentioning

confidence: 99%

Quantitative analysis of the factors which affect the interpatient organ‐at‐risk dose sparing variation in IMRT plans

et al. 2012

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Section: Iib Factors That Affect Oar Dosesmentioning

confidence: 99%

Quantitative analysis of the factors which affect the interpatient organ‐at‐risk dose sparing variation in IMRT plans

et al. 2012

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“…Although the concept of PTsymmetric quantum mechanics, as a fundamental theory, is still under a heated debate [27], it has been explored in optics and electronics by means of interleaving balanced gain-and-loss regions. Several interesting physical features have been explored, such as power oscillations [28], unidirectional invisibility [29][30][31][32], the reconfigurable Talbot effect [33], and coherent perfect laser absorbers [34,35]. Moreover, in the nonlinear domain, PT symmetry has been used to realize potential optical isolators and circulators [36][37][38][39][40].…”

Section: Introductionmentioning

confidence: 99%

PT-Symmetric Acoustics

et al. 2014

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We introduce here the concept of acoustic parity-time (PT ) symmetry and demonstrate the extraordinary scattering characteristics of the acoustic PT medium. On the basis of exact calculations, we show how an acoustic PT -symmetric medium can become unidirectionally transparent at given frequencies. Combining such a PT -symmetric medium with transformation acoustics, we design two-dimensional symmetric acoustic cloaks that are unidirectionally invisible in a prescribed direction. Our results open new possibilities for designing functional acoustic devices with directional responses.

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“…However, 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 not only on its distance from the PTV surface but also on the treatment beam orientation [ 5 , 7 , 8 ].…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2020

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Purpose In 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 materials One 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). Results For 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. Conclusion The 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.

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Response to “Comment on ‘A planning quality evaluation tool for prostate adaptive IMRT based on machine learning’ ” [Med. Phys. 38, 719 (2011)]

Cited by 11 publications

References 4 publications

Quantitative analysis of the factors which affect the interpatient organ‐at‐risk dose sparing variation in IMRT plans

Quantitative analysis of the factors which affect the interpatient organ‐at‐risk dose sparing variation in IMRT plans

PT-Symmetric Acoustics

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

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