Pretreatment intensity-modulated radiotherapy quality assurance is performed using simple rectangular or cylindrical phantoms; thus, the dosimetric errors caused by complex patient-specific anatomy are absent in the evaluation objects. In this study, we construct a system for generating patient-specific three-dimensional (3D)-printed phantoms for radiotherapy dosimetry. An anthropomorphic head phantom containing the bone and hollow of the paranasal sinus is scanned by computed tomography (CT). Based on surface rendering data, a patient-specific phantom is formed using a fused-deposition-modeling-based 3D printer, with a polylactic acid filament as the printing material. Radiophotoluminescence glass dosimeters can be inserted in the 3D-printed phantom. The phantom shape, CT value, and absorbed doses are compared between the actual and 3D-printed phantoms. The shape difference between the actual and printed phantoms is less than 1 mm except in the bottom surface region. The average CT value of the infill region in the 3D-printed phantom is -6 ± 18 Hounsfield units (HU) and that of the vertical shell region is 126 ± 18 HU. When the same plans were irradiated, the dose differences were generally less than 2%. These results demonstrate the feasibility of the 3D-printed phantom for artificial in vivo dosimetry in radiotherapy quality assurance.
Glasses are known to undergo spontaneous densification during isothermal annealing. This volume recovery process can be viewed as collapse of free volume. We have modeled this change in free volume during annealing as a vacancy diffusion process, where the diffusion constant depends on the local free volume (as defined by the Simha-Somcynsky theory) through the Doolittle equation. Good agreement was obtained with estimates of experimental volume recovery results for poly(vinyl acetate). Since volume recovery is sample-size independent, the characteristic length for diffusion cannot be identified with macroscopic dimensions. Several possibilities exist that can reconcile a diffusion picture with the known sample-size independence of the volume recovery process. These possibilities include internal annihilation of vacancies, density fluctuations, and a coupling of diffusive and uniform lattice motions.
A radiophotoluminescent glass dosimeter (RGD) is widely used in postal audit system for photon beams in Japan. However, proton dosimetry in RGDs is scarcely used owing to a lack of clarity in their response to beam quality. In this study, we investigated RGD response to beam quality for establishing a suitable linear energy transfer (LET)-corrected dosimetry protocol in a therapeutic proton beam.The RGD response was compared with ionization chamber measurement for a 100-225 MeV passive proton beam. LET of the measurement points was calculated by the Monte Carlo method. An LET-correction factor, defined as a ratio between the non-corrected RGD dose and ionization chamber dose, of 1.226 × (LET) − 0.171 was derived for the RGD response. The magnitude of the LET-dependence of RGD increased with LET; for an LET of 8.2 keV/μm, the RGD under-response was up to 16%. The coefficient of determination, mean difference ± SD of non-corrected RGD dose, residual range-corrected RGD dose, and LET-corrected RGD dose to the ionization chamber are 0.923, 3.7 ± 4.2%, − 2.4 ± 7.5%, and 0.04 ± 2.1%, respectively. The LET-corrected RGD dose was within 5% of the corresponding ionization chamber dose at all energies until 200 MeV, where it was 5.3% lower than the ionization chamber dose.A corrected LET-dependence of RGD using a correction factor based on a power function of LET and precise dosimetric verification close to the maximum LET were realized here. We further confirmed establishment of an accurate postal audit under various irradiation conditions.
To establish a predictive model for pain response following radiotherapy using a combination of radiomic and clinical features of spinal metastasis. This retrospective study enrolled patients with painful spine metastases who received palliative radiation therapy from 2018 to 2019. Pain response was defined using the International Consensus Criteria. The clinical and radiomic features were extracted from medical records and pre-treatment CT images. Feature selection was performed and a random forests ensemble learning method was used to build a predictive model. Area under the curve (AUC) was used as a predictive performance metric. 69 patients were enrolled with 48 patients showing a response. Random forest models built on the radiomic, clinical, and ‘combined’ features achieved an AUC of 0.824, 0.702, 0.848, respectively. The sensitivity and specificity of the combined features model were 85.4% and 76.2%, at the best diagnostic decision point. We built a pain response model in patients with spinal metastases using a combination of clinical and radiomic features. To the best of our knowledge, we are the first to examine pain response using pre-treatment CT radiomic features. Our model showed the potential to predict patients who respond to radiation therapy.
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