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shows sizes of ITV conv , ITV GM , ITV SRA , and ITV MRA . Average
Source publication
To estimate the lung tumor position from multiple anatomical features on four-dimensional computed tomography (4D-CT) data sets using single regression analysis (SRA) and multiple regression analysis (MRA) approach and evaluate an impact of the approach on internal target volume (ITV) for stereotactic body radiotherapy (SBRT) of the lung. Eleven co...
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Background:
Mobility of lung tumors is induced by respiration and causes inadequate dose coverage.
Objective:
This study quantified lung tumor motion, velocity, and stability for small (≤5 cm) and large (>5 cm) tumors to adapt radiation therapy techniques for lung cancer patients.
Material and methods:
In this retrospective study, 70 patients...
Citations
... In principle, if imaging and treatment are synchronized with the patient's respiratory cycle, there is the potential for CTV-PTV margin reduction. (19). ...
Background/Aim. Four dimensional (4DCT) simulation is a useful tool for motion assessment in lung cancer radiotherapy. Conventional Three dimensional (3D) - Free Breathing simulation is static, with limited motion information. The aim of this study was to compare clinically significant differences between the target volumes defined on 3D CT vs. 4D CT simulation and potential impact on the planning target volume (PTV). In addition, to quantify movements of primary tumour (GTV) during 4D CT simulation on three axis -Z-supero inferior (SI), X-mediolateral (ML), and Y-anteroposterior (AP). Methods. This retrospective study evaluated 20 lung cancer patients who underwent CT simulation for radical radiotherapy treatment. Free Breathing 3D CT and 4D CT simulation were acquired for each patient in accordance with our institutional protocol. Volumetric comparison radiation volumes defined on 3D CT vs. 4D CT simulation was done-Gross tumour volume GTV 3D vs. internal GTV- (iGTV 4D) and PTV 3D vs. iPTV 4D. Volumetric values expressed in cm3 and equivalent spherical diameter (ESD) expressed in cm were assessed. Comparison of GTV movement in the phase FB-GTV FB, phase 0-GTV0, phase 50-GTV 50, and phase Maximum intensity projection (MIP) -GTV MIP was made with GTV FB as the basic value. The evaluation was made in three axis. Results. Comparison volumetric values between GTV 3D vs. iGTV 4D was 63.15 vs.85.51 (p
... [24][25][26] MRA is a widely used linear analysis method that also provides fast prediction. [27][28][29] Both RTA and MRA can be considered as traditional machine learning methods. NNs comprise an artificial analysis method and are being widely used in medical physics. ...
... In the MRA method, every value of the independent multiple predictor variables is associated with a dependent objective variable. [27][28][29] The MRA equation is given by ...
Purpose
The dosimetric accuracies of volumetric modulated arc therapy (VMAT) plans were predicted using plan complexity parameters via machine learning.
Methods
The dataset consisted of 600 cases of clinical VMAT plans from a single institution. The predictor variables (n = 28) for each plan included complexity parameters, machine type, and photon beam energy. Dosimetric measurements were performed using a helical diode array (ArcCHECK), and the dosimetric accuracy of the passing rates for a 5% dose difference (DD5%) and gamma index of 3%/3 mm (γ3%/3 mm) were predicted using three machine learning models: regression tree analysis (RTA), multiple regression analysis (MRA), and neural networks (NNs). First, the prediction models were applied to 500 cases of the VMAT plans. Then, the dosimetric accuracy was predicted using each model for the remaining 100 cases (evaluation dataset). The error between the predicted and measured passing rates was evaluated.
Results
For the 600 cases, the mean ± standard deviation of the measured passing rates was 92.3% ± 9.1% and 96.8% ± 3.1% for DD5% and γ3%/3 mm, respectively. For the evaluation dataset, the mean ± standard deviation of the prediction errors for DD5% and γ3%/3 mm was 0.5% ± 3.0% and 0.6% ± 2.4% for RTA, 0.0% ± 2.9% and 0.5% ± 2.4% for MRA, and –0.2% ± 2.7% and –0.2% ± 2.1% for NN, respectively.
Conclusions
NNs performed slightly better than RTA and MRA in terms of prediction error. These findings may contribute to increasing the efficiency of patient‐specific quality‐assurance procedures.
... However, 4DCT can collect the motion information during the complete motion period. The reconstructed images can dynamically demonstrate the trajectory in the motion cycle, which can reflect the motion range of tumors in the three-dimensional directions (8). ...
Differences in gross target volume (GTV) and central point positions among moving lung cancer models constructed by CT scanning at different frequencies were compared, in order to explore the effect of different respiratory frequencies on the GTV constructions in moving lung tumors. Eight models in different shapes and sizes were established to stimulate lung tumors. The three-dimensional computed tomography (3DCT) and four-dimensional computed tomography (4DCT) scanning were performed at 10, 15 and 20 times/min in different models. Differences in GTV volumes and central point positions at different motion frequencies were compared by means of GTV3Ds (GTV3D-10, GTV3D-15, GTV3D-20) and IGTV4Ds (IGTV4D-10, IGTV4D-15, IGTV4D-20). Volumes of GTV3D-10, GTV3D-15, GTV3D-20 were 12.41±14.26, 10.38±11.18 and 12.50±15.23 cm³ respectively (P=0.687). Central point coordinates in the x-axis direction were-8.16±96.21,-8.57±96.08 and-8.56±95.73 respectively (P=0.968). Central point coordinates in the y-axis direction were 108.22±25.03, 110.41±22.47 and 109.04±24.24 (P=0.028). Central point coordinates in the z-axis direction were 65.19±13.68, 65.43±13.40 and 65.38±13.17 (P=0.902). The difference was significant in the y-axis direction (P=0.028). Volumes of IGTV4D-10, IGTV4D-15, IGTV4D-20 were 17.78±19.42, 17.43±19.56 and 17.44±18.80 cm³ (P=0.417). Central point coordinates in the x-axis direction were-7.73±95.93,-7.86±95.56 and-7.92±95.14 (P=0.325). Central point coordinates in the y-axis direction were 109.41±24.54, 109.60±24.13 and 109.16±24.28 (P=0.525). Central point coordinates in the z-axis direction were 65.52±13.31, 65.59±13.39 and 65.51±13.34 (P=0.093). However, the central point position of GTV in the head and foot direction by 3DCT scanning was severely affected by the respiratory frequency.
Purpose
To develop a prediction model (PM) for target positioning using diaphragm waveforms extracted from CBCT projection images.
Methods
Nineteen patients with lung cancer underwent orthogonal rotational kV x‐ray imaging lasting 70 s. IR markers placed on their abdominal surfaces and an implanted gold marker located nearest to the tumor were considered as external surrogates and the target, respectively. Four different types of regression‐based PM were trained using surrogate motions and target positions for the first 60 s, as follows: Scenario A : Based on the clinical scenario, 3D target positions extracted from projection images were used as they were (PM CL ). Scenario B : The short‐arc 4D‐CBCT waveform exhibiting eight target positions was obtained by averaging the target positions in Scenario A . The waveform was repeated for 60 s ( W 4D‐CBCT ) by adapting to the respiratory phase of the external surrogate. W 4D‐CBCT was used as the target positions (PM 4D‐CBCT ). Scenario C : The Amsterdam Shroud (AS) signal, which depicted the diaphragm motion in the superior–inferior direction was extracted from the orthogonal projection images. The amplitude and phase of W 4D‐CBCT were corrected based on the AS signal. The AS‐corrected W 4D‐CBCT was used as the target positions (PM AS‐4D‐CBCT ). Scenario D : The AS signal was extracted from single projection images. Other processes were the same as in Scenario C . The prediction errors were calculated for the remaining 10 s.
Results
The 3D prediction error within 3 mm was 77.3% for PM 4D‐CBCT , which was 12.8% lower than that for PM CL . Using the diaphragm waveforms, the percentage of errors within 3 mm improved by approximately 7% to 84.0%‐85.3% for PM AS‐4D‐CBCT in Scenarios C and D , respectively. Statistically significant differences were observed between the prediction errors of PM 4D‐CBCT and PM AS‐4D‐CBCT .
Conclusion
PM AS‐4D‐CBCT outperformed PM 4D‐CBCT , proving the efficacy of the AS signal‐based correction. PM AS‐4D‐CBCT would make it possible to predict target positions from 4D‐CBCT images without gold markers.
