A method for simulating spot-scanned delivery to a moving tumour was developed which uses patient-specific image and plan data. The magnitude of interplay effects was investigated for two patient cases under different fractionation and respiratory motion variation scenarios. The use of volumetric rescanning for motion mitigation was also investigated. For different beam arrangements, interplay effects lead to severely distorted dose distributions for a single fraction delivery. Baseline shift variations for single fraction delivery reduced the dose to the clinical target volume (CTV) by up to 14.1 Gy. Fractionated delivery significantly reduced interplay effects; however, local overdosage of 12.3% compared to the statically delivered dose remained for breathing period variations. Variations of the tumour baseline position and respiratory period were found to have the largest influence on target inhomogeneity; these effects were reduced with fractionation. Volumetric rescanning improved the dose homogeneity. For the CTV, underdosage was improved by up to 34% in the CTV and overdosage to the lung was reduced by 6%. Our results confirm that rescanning potentially increases the dose homogeneity; however, it might not sufficiently compensate motion-induced dose distortions. Other motion mitigation techniques may be required to additionally treat lung tumours with scanned proton beams.

The helical tomotherapy (HT) Hi-ART system was installed at our department in April 2007. In July 2018 the first Radixact system in Germany has been launched for clinical use. We present differences, advantages and disadvantages and show future perspectives in patient treatment using two HT devices. We investigate patient characteristics, image quality, radiotherapy treatment specifications and analyze the time effort for treatments with the Hi-ART system from April 2010 until May 2017 and compare it to the data acquired in the first nine months of usage of the Radixact system. Comparing the Hi-ART and Radixact system, the unique option of integrated MVCT image acquisition has experienced distinct improvement in image quality. Time effort for irradiation treatment could be improved resulting in a mean beam on time for craniospinal axis treatment of 636.2 s for the Radixact system compared to 915.9 s for the Hi-ART system. The beneficial use of tomotherapy for complex target volumes is demonstrated by a head and neck tumor case and craniospinal axis treatment. With the Radixact system MVCT image quality has been improved allowing for fast and precise interfraction dose adaptation. The improved time effort for patient treatment could increase the accessibility for clinical usage.

To fully exploit the advantages of magnetic resonance imaging (MRI) for radiotherapy (RT) treatment planning, a method is required to overcome the problem of lacking electron density information. We aim to establish and evaluate a new method for computed tomography (CT) data generation based on MRI and image registration. The thereby generated CT data is used for dose accumulation. We developed a process flow based on an initial pair of rigidly co-registered CT and T2-weighted MR image representing the same anatomical situation. Deformable image registration using anatomical landmarks is performed between the initial MRI data and daily MR images. The resulting transformation is applied to the initial CT, thus fractional CT data is generated. Furthermore, the dose for a photon intensity modulated RT (IMRT) or intensity modulated proton therapy (IMPT) plan is calculated on the generated fractional CT and accumulated on the initial CT via inverse transformation. The method is evaluated by the use of phantom CT and MRI data. Quantitative validation is performed by evaluation of the mean absolute error (MAE) between the measured and the generated CT. The effect on dose accumulation is examined by means of dose-volume parameters. One patient case is presented to demonstrate the applicability of the method introduced here. Overall, CT data derivation lead to MAEs with a median of 37.0 HU ranging from 29.9 to 66.6 HU for all investigated tissues. The accuracy of image registration showed to be limited in the case of unexpected air cavities and at tissue boundaries. The comparisons of dose distributions based on measured and generated CT data agree well with the published literature. Differences in dose volume parameters kept within 1.6% and 3.2% for photon and proton RT, respectively. The method presented here is particularly suited for application in adaptive RT in current clinical routine, since only minor additional technical equipment is required.

Introduction: Pneumonitis is a relevant side effect after radiotherapy (RT) and immunotherapy with checkpoint inhibitors (ICIs). Since the effect is radiation dose dependent, the risk increases for high fractional doses as applied for stereotactic body radiation therapy (SBRT) and might even be enhanced for the combination of SBRT with ICI therapy. Hence, patient individual pre-treatment prediction of post-treatment pneumonitis (PTP) might be able to support clinical decision making. Dosimetric factors, however, use limited information and, thus, cannot exploit the full potential of pneumonitis prediction.Methods: We investigated dosiomics and radiomics model based approaches for PTP prediction after thoracic SBRT with and without ICI therapy. To overcome potential influences of different fractionation schemes, we converted physical doses to 2 Gy equivalent doses (EQD2) and compared both results. In total, four single feature models (dosiomics, radiomics, dosimetric, clinical factors) were tested and five combinations of those (dosimetric+clinical factors, dosiomics+radiomics, dosiomics+dosimetric+clinical factors, radiomics+dosimetric+clinical factors, radiomics+dosiomics+dosimetric+clinical factors). After feature extraction, a feature reduction was performed using pearson intercorrelation coefficient and the Boruta algorithm within 1000-fold bootstrapping runs. Four different machine learning models and the combination of those were trained and tested within 100 iterations of 5-fold nested cross validation.Results: Results were analysed using the area under the receiver operating characteristic curve (AUC). We found the combination of dosiomics and radiomics features to outperform all other models with AUC_{radiomics+dosiomics, D} = 0.79 (95% confidence interval 0.78-0.80) and AUC_{radiomics+dosiomics, EQD2} = 0.77 (0.76-0.78) for physical dose and EQD2, respectively. ICI therapy did not impact the prediction result (AUC ≤ 0.5). Clinical and dosimetric features for the total lung did not improve the prediction outcome.Conclusion: Our results suggest that combined dosiomics and radiomics analysis can improve PTP prediction in patients treated with lung SBRT. We conclude that pre-treatment prediction could support clinical decision making on an individual patient basis with or without ICI therapy.

We investigated the potential of respiratory gating to mitigate the motion-caused misdosage in lung stereotactic body radiotherapy (SBRT). For fourteen patients with lung tumors, we investigated treatment plans for a gating window (GW) including three breathing phases around the maximum exhalation phase, GW40–60. For a subset of six patients, we also assessed a preceding three-phase GW20–40 and six-phase GW20–70. We analyzed the target volume, lung, esophagus, and heart doses. Using normal tissue complication probability (NTCP) models, we estimated radiation pneumonitis and esophagitis risks. Compared to plans without gating, GW40–60 significantly reduced doses to organs at risk without impairing the tumor doses. On average, the mean lung dose decreased by 0.6 Gy (p < 0.001), treated lung V20Gy by 2.4% (p = 0.003), esophageal dose to 5cc by 2.0 Gy (p = 0.003), and maximum heart dose by 3.2 Gy (p = 0.009). The model-estimated mean risks of 11% for pneumonitis and 12% for esophagitis without gating decreased upon GW40–60 to 7% and 9%, respectively. For the highest-risk patient, gating reduced the pneumonitis risk from 43% to 32%. Gating is most beneficial for patients with high-toxicity risks. Pre-treatment toxicity risk assessment may help optimize patient selection for gating, as well as GW selection for individual patients.

Objectives: The present study aims to evaluate long-term side-effects and outcomes and confirm prognostic factors after stereotactic body radiotherapy (SBRT) of pulmonary lesions. This is the first work that combines the investigated data from patient charts and patient-reported outcome (PRO) up to 14 years after therapy. Materials and methods: We analyzed 219 patients and 316 lung metastases treated between 2004 and 2019. The pulmonary lesions received a median dose and dose per fraction of 35 Gy (range: 14–60.5 Gy) and 8 Gy (range: 3–20 Gy) to the surrounding isodose. During the last 1.5 years of monitoring, we added PRO assessment to our follow-up routine. We sent an invitation to a web-based survey questionnaire to all living patients whose last visit was more than 6 months ago. Results: Median OS was 27.6 months. Univariate analysis showed a significant influence on OS for KPS ≥90%, small gross tumor volume (GTV) and planning target volume (PTV), the absence of external metastases, ≤3 pulmonary metastases, and controlled primary tumor. The number of pulmonary metastases and age influenced local control (LC) significantly. During follow-up, physicians reported severe side-effects ≥ grade 3 in only 2.9% within the first 6 months and in 2.5% after 1 year. Acute symptomatic pneumonitis grade 2 was observed in 9.7%, as grade 3 in 0.5%. During PRO assessment, 39 patients were contacted, 38 patients participated, 14 participated twice during follow-up. Patients reported 15 cases of severe side effects (grade ≥ 3) according to PROCTCAE classification. Severe dyspnea (n = 6) was reported mostly. Conclusion: We could confirm excellent local control and low toxicity rates. PROs improve and complement follow-up care. They are an essential measure in addition to the physician-reported outcomes. Future research must be conducted regarding the correct interpretation of PRO data.