Physica Medica-European Journal of Medical Physics最新文献

HollandPTC is an independent outpatient center for proton therapy, scientific research, and education. Patients with different types of cancer are treated with Intensity Modulated Proton Therapy (IMPT). Additionally, the HollandPTC R&D consortium conducts scientific research into the added value and improvements of proton therapy. To this end, HollandPTC created clinical and pre-clinical research facilities including a versatile R&D proton beamline for various types of biologically and technologically oriented research. In this work, we present the characterization of the R&D proton beamline of HollandPTC. Its pencil beam mimics the one used for clinical IMPT, with energy ranging from 70 up to 240 MeV, which has been characterized in terms of shape, size, and intensity. For experiments that need a uniform field in depth and lateral directions, a dual ring passive scattering system has been designed, built, and characterized. With this system, field sizes between 2 × 2 cm² and 20 × 20 cm² with 98 % uniformity are produced with dose rates ranging from 0.5 Gy/min up to 9 Gy/min. The unique and customized support of the dual ring system allows switching between a pencil beam and a large field in a very simple and fast way, making the HollandPTC R&D proton beam able to support a wide range of different applications.

Purpose: To investigate the performance of a machine learning-based segmentation method for treatment planning of gastric cancer.

Materials and methods: Eighteen patients planned to be irradiated for gastric cancer were studied. The target and the surrounding organs-at-risk (OARs) were manually delineated on CT scans. A machine learning algorithm was used for automatically segmenting the lungs, kidneys, liver, spleen and spinal cord. Two radiation oncologists evaluated these contours and performed the required editing. The accuracy of the auto-segmented contours relative to manual outlines was evaluated by calculating the dice similarity coefficient (DSC), Jaccard score (JS), sensitivity and precision. VMAT plans were initially created on manual contours (MCPlans) and, then, on edited and unedited auto-segmented contours (AC_edPlans). Dose parameters of the OARs and target volume derived from the different treatment plans were statistically compared.

Results: The 24.6 % of the auto-segmented contours were acceptable and 40.5 % needed changes related to stylistic deviations. Minor editing was applied in 34.1 % of these contours. The mean values of the DSC, JS, sensitivity and precision associated with the comparison of the manual outlines and the contour set including edited and unedited auto-segmented contours were 0.91-0.97, 0.84-0.94, 0.92-0.97 and 0.91-0.97, respectively. No significant differences were found for fifteen out of eighteen examined dosimetric parameters derived from MCPlans and AC_edPlans (p > 0.05). These parameters from the MCPlans agreed well with those from AC_edPlans based on the Bland-Altman test.

Conclusions: The qualitative, quantitative and dosimetric analysis highlighted the clinical acceptability of a machine learning-based segmentation method for radiotherapy of gastric cancer.

Purpose: This work aims at investigating, via in-silico evaluations, the noise properties of an innovative scanning geometry in cone-beam CT (CBCT): eCT. This scanning geometry substitutes each of the projections in CBCT with a series of collimated projections acquired over an oscillating scanning trajectory. The analysis focused on the impact of the number of the projections per period (PP) on the noise characteristics.

Methods: In-silico eCT scanner was simulated with a GPU based Monte Carlo software. We employed two homogeneous PMMA phantoms with a diameter of 12 cm and 16 cm whose tomographic images were reconstructed via an in-house developed software. Noise properties of the reconstructed volumes were evaluated in terms of coefficient of variation (COV), non-uniformity index , noise power spectrum (NPS), and null-cone over the 3D NPS.

Results: The beam narrowing at higher PP led to a significant reduction of cupping artifacts, with a non-uniformity index reducing of about 33% going from conventional CBCT to PP = 10. Oscillating scan orbits almost fully recovered missing data in conventional CBCT, with a narrowing of the null-cone in 3D NPS to below 2.5% for PP ≥ 5 compared to 11.0% in conventional CBCT at 6.5 cm from the orbit plane CONCLUSIONS: The work characterizes the noise in reconstructed 3D images in eCT, with particular focus on the NPS. The impact of the beam collimation on cupping artifacts reduction is outlined. Similarly, the missing data outlined by the null-cone is considerably narrowed in comparison to conventional CBCT, especially for portions of the FOV far from the middle-reconstructed plane.

Purpose: It is possible to combine theoretical models with Monte Carlo simulations to investigate the relationship between radiation-induced initial DNA damage and cell survival. Several combinations of models have been proposed in recent years, sparking interest in comparing their predictions in view of future clinical applications.

Methods: Two in silico methods for calculating cell survival fractions were optimized for proton irradiation of the Chinese hamster V79 cell line, for LET values ranging from 3.40 and 100 keV/μm. These methods, based on different Monte Carlo codes and theoretical models, were benchmarked against published V79 cell survival data to identify the sources of discrepancies.

Results: The predictive capacities of the methods were evaluated for several proton LET values using an external dataset. After recalibrating model parameters, multiple methods were assessed. This approach helped identify sources of variation, the main one being the simulated number of DSBs, which differed by a factor up to 3 between the two Monte Carlo codes. In this process a new method was defined, that, in all but one case, allows for a reduction in prediction error of up to 56%. Additionally, a freely available GUI for computing cell survival was refined, to facilitate further comparison of diverse theoretical models.

Conclusion: The systematic comparison of two predictive chains, characterized by distinct applicability ranges and features, was conducted. Optimization and analysis of various combinations were undertaken to elucidate differences. Addressing and minimizing such discrepancies will be crucial for further enhancing the reliability of predictive models of cell survival, aiming for biologically informed treatment planning.

Purpose: We investigate the feasibility of using a biophysically guided approach for delineating the Clinical Target Volume (CTV) in Glioblastoma Multiforme (GBM) by evaluating its impact on the treatment outcomes, specifically Overall Survival (OS) time.

Methods: An established reaction-diffusion model was employed to simulate the spatiotemporal evolution of cancerous regions in T1-MRI images of GBM patients. The effects of the parameters of this model on the simulated tumor borders were quantified and the optimal values were used to estimate the distribution of infiltrative cells (CTVmodel). Radiomics and deep learning models were examined to predict the OS time by analyzing the GTV, clinical CTV, and CTVmodel.

Results: The study involves 126 subjects for model development and 62 independent subjects for testing. Evaluation of the proposed approach demonstrates comparable predictive power for OS time that is achieved with the clinically defined CTV. Specifically, for the binary survival prediction, short vs. long time, the proposed CTVmodelresulted in improvements of prognostic power, in terms of AUROC, both for the validation (0.77 from 0.75) and the testing (0.73 from 0.71) set. Quantitative comparisons for three-class prediction and survival regression models exhibited a similar trend of comparable performance.

Conclusion: The findings highlight the potential of biophysical modeling for estimating and incorporating the spread of infiltrating cells into CTV delineation. Further clinical investigations are required to validate the clinical efficacy.

Purpose: This study aimed to determine the effect of model selection on simplified dosimetry for the kidneys using Bayesian fitting (BF) and single-time-point (STP) imaging.

Methods: Kidney biokinetics data of [¹⁷⁷Lu]Lu-PSMA-617 from mHSPC were collected using SPECT/CT after injection of (3.1 ± 0.1) GBq at time points T1(2.3 ± 0.5), T2(23.8 ± 2.0), T3(47.7 ± 2.2), T4(71.8 ± 2.2), and T5(167.4 ± 1.9) h post-injection. Eleven functions with various parameterizations and a combination of shared and individual parameters were used for model selection. Model averaging of functions with an Akaike weight of >10 % was used to calculate the reference TIAC (TIAC_REF). STP BF method (STP-BF) was performed to determine the STP TIACs (TIAC_STP-BF). The STP-BF performance was assessed by calculating the root-mean-square error (RMSE) of relative deviation between TIAC_STP-BF and TIAC_REF. In addition, the STP-BF performance was compared to the Hänscheid Method.

Results: The function [Formula: see text] with shared parameter λ₂ was selected as the best function (Akaike weight of 57.91 %). STP-BF using the best function resulted in RMSEs of 20.3 %, 9.1 %, 8.4 %, 13.6 %, and 19.3 % at T1, T2, T3, T4, and T5, respectively. The RMSEs of STP-Hänscheid were 22.4 %, 14.6 %, and 21.9 % at T2, T3, and T4, respectively.

Conclusion: A model selection was presented to determine the fit function for calculating TIACs in STP-BF. This study shows that the STP dosimetry using BF and model selection performed better than the frequently used STP Hänscheid method.

Background: FLASH radiotherapy necessitates the development of advanced Quality Assurance methods and detectors for accurate monitoring of the radiation field. This study introduces enhanced time-resolution detection systems and methods used to measure the delivered number of pulses, investigate temporal structure of individual pulses and dose-per-pulse (DPP) based on secondary radiation particles produced in the experimental room.

Methods: A 20 MeV electron beam generated from a linear accelerator (LINAC) was delivered to a water phantom. Ultra-high dose-per-pulse electron beams were used with a dose-per-pulse ranging from ̴ 1 Gy to over 7 Gy. The pulse lengths ranged from 1.18 µs to 2.88 µs at a pulse rate frequency of 5 Hz. A semiconductor pixel detector Timepix3 was used to track single secondary particles. Measurements were performed in the air, while the detector was positioned out-of-field at a lateral distance of 200 cm parallel with the LINAC exit window. The dose deposited was measured along with the pulse length and the nanostructure of the pulse.

Results: The time of arrival (ToA) of single particles was measured with a resolution of 1.56 ns, while the deposited energy was measured with a resolution of several keV based on the Time over Threshold (ToT) value. The pulse count measured by the Timepix3 detector corresponded with the delivered values, which were measured using an in-flange integrating current transformer (ICT). A linear response (R² = 0.999) was established between the delivered beam current and the measured dose at the detector position (orders of nGy). The difference between the average measured and delivered pulse length was ∼0.003(30) μs.

Conclusion: This simple non-invasive method exhibits no limitations on the delivered DPP within the range used during this investigation.

Background: Recent studies in the field of lung cancer have emphasized the important role of body composition, particularly fatty tissue, as a prognostic factor. However, there is still a lack of practice in combining fatty tissue to discriminate benign and malignant pulmonary nodules.

Purpose: This study proposes a deep learning (DL) approach to explore the potential predictive value of dual imaging markers, including intrathoracic fat (ITF), in patients with pulmonary nodules.

Methods: We enrolled 1321 patients with pulmonary nodules from three centers. Image feature extraction was performed on computed tomography (CT) images of pulmonary nodules and ITF by DL, multimodal information was used to discriminate benign and malignant in patients with pulmonary nodules.

Results: Here, the areas under the receiver operating characteristic curve (AUC) of the model for ITF combined with pulmonary nodules were 0.910(95 % confidence interval [CI]: 0.870-0.950, P = 0.016), 0.922(95 % CI: 0.883-0.960, P = 0.037) and 0.899(95 % CI: 0.849-0.949, P = 0.033) in the internal test cohort, external test cohort1 and external test cohort2, respectively, which were significantly better than the model for pulmonary nodules. Intrathoracic fat index (ITFI) emerged as an independent influencing factor for benign and malignant in patients with pulmonary nodules, correlating with a 9.4 % decrease in the risk of malignancy for each additional unit.

Conclusion: This study demonstrates the potential auxiliary predictive value of ITF as a noninvasive imaging biomarker in assessing pulmonary nodules.

Purpose: Understanding cell cycle variations in radiosensitivity is important for α-particle therapies. Differences are due to both repair response mechanisms and the quantity of initial radiation-induced DNA strand breaks. Genome compaction within the nucleus has been shown to impact the yield of strand breaks. Compaction changes during the cell cycle are therefore likely to contribute to radiosensitivity differences. Simulation allows the strand break yield to be calculated independently of repair mechanisms which would be challenging experimentally.

Methods: Using Geant4 the impact of genome compaction changes on strand break induction due to α-particles was simulated. Genome compaction is considered to be described by three metrics: global base pair density, chromatin fibre packing fraction and chromosome condensation. Nuclei in the G1, S, G2 and M phases from two cancer cell lines and one normal cell line are simulated. Repair mechanisms are not considered to study only the impact of genome compaction changes.

Results: The three compaction metrics have differing effects on the strand break yield. For all cell lines the strand break yield is greatest in G2 cells and least in G1 cells. More strand breaks are induced in the two cancer cell lines than in the normal cell line.

Conclusions: Compaction of the genome affects the initial yield of strand breaks. Some radiosensitivity differences between cell lines can be attributed to genome compaction changes between the phases of the cell cycle. This study provides a basis for further analysis of how repair deficiencies impact radiation-induced lethality in normal and malignant cells.