Survey on fan-beam computed tomography for radiotherapy: Current implementation and future perspectives of motion management and surface guidance devices.

Background and purpose: This work reports on the results of a survey performed on the use of computed tomography (CT) imaging for motion management, surface guidance devices, and their quality assurance (QA). Additionally, it details the collected user insights regarding professional needs in CT for radiotherapy. The purpose of the survey is to understand current practice, professional needs and future directions in the field of fan-beam CT in radiation therapy (RT). Materials and methods: An online institutional survey was conducted between 1-Sep-2022 and 10-Oct-2022 among medical physics experts at Belgian and Dutch radiotherapy institutions, to assess the current status, challenges, and future directions of motion management and surface image-guided radiotherapy. The survey consisted of a maximum of 143 questions, with the exact number depending on participants' responses. Results: The response rate was 66 % (31/47). Respiratory management was reported as standard practice in all but one institution; surface imaging during CT-simulation was reported in ten institutions. QA procedures are applied with varying frequencies and methodologies, primarily with commercial anatomy-like phantoms. Surface guidance users report employing commercial static and dynamic phantoms. Four main subjects are considered clinically important by the respondents: surface guidance, CT protocol optimisation, implementing gated imaging (4DCT, breath-hold), and a tattoo-less workflow. Conclusions: The survey highlights the scattered pattern of QA procedures for respiratory motion management, indicating the need for well-defined, unambiguous, and practicable guidelines. Surface guidance is considered one of the most important techniques that should be implemented in the clinical radiotherapy simulation workflow.

Survey on fan-beam computed tomography for radiotherapy: Imaging for dose calculation and delineation.

Background and purpose: To obtain an understanding of current practice, professional needs and future directions in the field of fan-beam CT in RT, a survey was conducted. This work presents the collected information regarding the use of CT imaging for dose calculation and structure delineation. Materials and methods: An online institutional survey was distributed to medical physics experts employed at Belgian and Dutch radiotherapy institutions to assess the status, challenges, and future directions of QA practices for fan-beam CT. A maximum of 143 questions covered topics such as CT scanner availability, CT scanner specifications, QA protocols, treatment simulation workflow, and radiotherapy dose calculation. Answer forms were collected between 1-Sep-2022 and 10-Oct-2022. Results: A 66 % response rate was achieved, yielding data on a total of 58 CT scanners. For MV photon therapy, all single-energy CT scans are reconstructed in Hounsfield Units for delineation or dose calculation, and a direct- or stoichiometric method was used to convert CT numbers for dose calculation. Limited use of dual-energy CT is reported for photon (N = 3) and proton dose calculations (N = 1). For brachytherapy, most institutions adopt water-based dose calculation, while approximately 26 % of the institutions take tissue heterogeneity into account. Commissioning and regular QA include eleven tasks, which are performed by two or more professions (29/31) with varying frequencies. Conclusions: Dual usage of a planning CT limits protocol optimization for both tissue characterization and delineation. DECT has been implemented only gradually. A variation of QA testing frequencies and tests are reported.

Machine-learning-based prediction of the effectiveness of the delivered dose by exhale-gated radiotherapy for locally advanced lung cancer: The additional value of geometric over dosimetric parameters alone.

Purpose: This study aimed to assess interfraction stability of the delivered dose distribution by exhale-gated volumetric modulated arc therapy (VMAT) or intensity-modulated arc therapy (IMAT) for lung cancer and to determine dominant prognostic dosimetric and geometric factors. Methods: Clinical target volume (CTVPlan) from the planning CT was deformed to the exhale-gated daily CBCT scans to determine CTVi, treated by the respective dose fraction. The equivalent uniform dose of the CTVi was determined by the power law (gEUDi) and cell survival model (EUDiSF) as effectiveness measure for the delivered dose distribution. The following prognostic factors were analyzed: (I) minimum dose within the CTVi (Dmin_i), (II) Hausdorff distance (HDDi) between CTVi and CTVPlan, (III) doses and deformations at the point in CTVPlan at which the global minimum dose over all fractions per patient occurs (PDmin_global_i), and (IV) deformations at the point over all CTVi margins per patient with the largest Hausdorff distance (HDPworst). Prognostic value and generalizability of the prognostic factors were examined using cross-validated random forest or multilayer perceptron neural network (MLP) classifiers. Dose accumulation was performed using back deformation of the dose distribution from CTVi to CTVPlan. Results: Altogether, 218 dose fractions (10 patients) were evaluated. There was a significant interpatient heterogeneity between the distributions of the normalized gEUDi values (p<0.0001, Kruskal-Wallis tests). Accumulated gEUD over all fractions per patient was 1.004-1.023 times of the prescribed dose. Accumulation led to tolerance of ~20% of fractions with gEUDi <93% of the prescribed dose. Normalized Dmin >60% was associated with predicted gEUD values above 95%. Dmin had the highest importance for predicting the gEUD over all analyzed prognostic parameters by out-of-bag loss reduction using the random forest procedure. Cross-validated random forest classifier based on Dmin as the sole input had the largest Pearson correlation coefficient (R=0.897) in comparison to classifiers using additional input variables. The neural network performed better than the random forest classifier, and the gEUD values predicted by the MLP classifier with Dmin as the sole input were correlated with the gEUD values characterized by R=0.933 (95% CI, 0.913-0.948). The performance of the full MLP model with all geometric input parameters was slightly better (R=0.952) than that based on Dmin (p=0.0034, Z-test). Conclusion: Accumulated dose distributions over the treatment series were robust against interfraction CTV deformations using exhale gating and online image guidance. Dmin was the most important parameter for gEUD prediction for a single fraction. All other parameters did not lead to a markedly improved generalizable prediction. Dosimetric information, especially location and value of Dmin within the CTV i , are vital information for image-guided radiation treatment.

The effects of motion on the dose distribution of proton radiotherapy for prostate cancer.

Proton radiotherapy of the prostate basal or whole seminal vesicles using scattering delivery systems is an effective treatment of prostate cancer that has been evaluated in prospective trials. Meanwhile, the use of pencil beam scanning (PBS) can further reduce the dose in the beam entrance channels and reduce the dose to the normal tissues. However, PBS dose distributions can be affected by intra- and interfractional motion. In this treatment planning study, the effects of intra- and interfractional organ motion on PBS dose distributions are investigated using repeated CT scans at close and distant time intervals. The minimum dose (Dmin) and the dose to 2% and 98% of the volumes (D2% and D98%), as well as EUD in the clinical target volumes (CTV), is used as measure of robustness. In all patients, D98% was larger than 96% and D2% was less than 106% of the prescribed dose. The combined information from Dmin, D98% and EUD led to the conclusion that there are no relevant cold spots observed in any of the verification plans. Moreover, it was found that results of single field optimization are more robust than results from multiple field optimizations.

Results of a multicentric in silico clinical trial (ROCOCO): comparing radiotherapy with photons and protons for non-small cell lung cancer.

INTRODUCTION: This multicentric in silico trial compares photon and proton radiotherapy for non-small cell lung cancer patients. The hypothesis is that proton radiotherapy decreases the dose and the volume of irradiated normal tissues even when escalating to the maximum tolerable dose of one or more of the organs at risk (OAR). METHODS: Twenty-five patients, stage IA-IIIB, were prospectively included. On 4D F18-labeled fluorodeoxyglucose-positron emission tomography-computed tomography scans, the gross tumor, clinical and planning target volumes, and OAR were delineated. Three-dimensional conformal radiotherapy (3DCRT) and intensity-modulated radiotherapy (IMRT) photon and passive scattered conformal proton therapy (PSPT) plans were created to give 70 Gy to the tumor in 35 fractions. Dose (de-)escalation was performed by rescaling to the maximum tolerable dose. RESULTS: Protons resulted in the lowest dose to the OAR, while keeping the dose to the target at 70 Gy. The integral dose (ID) was higher for 3DCRT (59%) and IMRT (43%) than for PSPT. The mean lung dose reduced from 18.9 Gy for 3DCRT and 16.4 Gy for IMRT to 13.5 Gy for PSPT. For 10 patients, escalation to 87 Gy was possible for all 3 modalities. The mean lung dose and ID were 40 and 65% higher for photons than for protons, respectively. CONCLUSIONS: The treatment planning results of the Radiation Oncology Collaborative Comparison trial show a reduction of ID and the dose to the OAR when treating with protons instead of photons, even with dose escalation. This shows that PSPT is able to give a high tumor dose, while keeping the OAR dose lower than with the photon modalities.

Design of and technical challenges involved in a framework for multicentric radiotherapy treatment planning studies.

This report introduces a framework for comparing radiotherapy treatment planning in multicentric in silico clinical trials. Quality assurance, data incompatibility, transfer and storage issues, and uniform analysis of results are discussed. The solutions that are given provide a useful guide for the set-up of future multicentric planning studies or public repositories of high quality data.