Dosimetric comparison of protons vs photons in re-irradiation of intracranial meningioma

Objectives: Re-irradiation of recurrent intracranial meningiomas represents a major challenge due to dose limits of critical structures and the necessity of sufficient dose coverage of the recurrent tumor for local control. The aim of this study was to investigate dosimetric differences between pencil beam scanning protons (PBS) and volumetric modulated arc therapy (VMAT) photons for intracranial re-irradiation of meningiomas. Methods: Nine patients who received an initial dose &gt;50 Gy for intracranial meningioma and who were re-irradiated for recurrence were selected for plan comparison. A volumetric modulated arc therapy photon and a pencil beam scanning proton plan were generated (prescription dose: 15 × 3 Gy) based on the targets used in the re-irradiation treatment. Results: In all cases, where the cumulative dose exceeded 100 or 90 Gy, these high dose volumes were larger for the proton plans. The integral doses were significantly higher in all photon plans (reduction with protons: 48.6%, p &lt; 0.01). In two cases (22.2%), organ at risk (OAR) sparing was superior with the proton plan. In one case (11.1%), the photon plan showed a dosimetric advantage. In the remaining six cases (66.7%), we found no clinically relevant differences in dose to the OARs. Conclusions: The dosimetric results of the accumulated dose for a re-irradiation with protons and with photons were very similar. The photon plans had a steeper dose falloff directly outside the target and were superior in minimizing the high dose volumes. The proton plans achieved a lower integral dose. Clinically relevant OAR sparing was extremely case specific. The optimal treatment modality should be assessed individually. Advances in knowledge: Dose sparing in re-irradiation of intracranial meningiomas with protons or photons is highly case specific and the optimal treatment modality needs to be assessed on an individual basis. </jats:sec

Complex Systems Science and Community-Based Research

There is an abundance of community-based research literature that incorporates complex system science concepts and techniques. However, currently there is a gap in how these concepts and techniques are being used, and, more broadly, how these two fields complement one another. The debate on how complex systems science meaningfully bolsters the deployment of community-based research has not yet reached consensus, therefore, we present a protocol for a new scoping review that will identify characteristics at the intersection of community-based research and complex systems science. This knowledge will enhance the understanding of how complex systems science, a quickly evolving field, is being utilized in community-based research and practice

PyRaDiSe: A Python package for DICOM-RT-based auto-segmentation pipeline construction and DICOM-RT data conversion.

BACKGROUND AND OBJECTIVE Despite fast evolution cycles in deep learning methodologies for medical imaging in radiotherapy, auto-segmentation solutions rarely run in clinics due to the lack of open-source frameworks feasible for processing DICOM RT Structure Sets. Besides this shortage, available open-source DICOM RT Structure Set converters rely exclusively on 2D reconstruction approaches leading to pixelated contours with potentially low acceptance by healthcare professionals. PyRaDiSe, an open-source, deep learning framework independent Python package, addresses these issues by providing a framework for building auto-segmentation solutions feasible to operate directly on DICOM data. In addition, PyRaDiSe provides profound DICOM RT Structure Set conversion and processing capabilities; thus, it applies also to auto-segmentation-related tasks, such as dataset construction for deep learning model training. METHODS The PyRaDiSe package follows a holistic approach and provides DICOM data handling, deep learning model inference, pre-processing, and post-processing functionalities. The DICOM data handling allows for highly automated and flexible handling of DICOM image series, DICOM RT Structure Sets, and DICOM registrations, including 2D-based and 3D-based conversion from and to DICOM RT Structure Sets. For deep learning model inference, extending given skeleton classes is straightforwardly achieved, allowing for employing any deep learning framework. Furthermore, a profound set of pre-processing and post-processing routines is included that incorporate partial invertibility for restoring spatial properties, such as image origin or orientation. RESULTS The PyRaDiSe package, characterized by its flexibility and automated routines, allows for fast deployment and prototyping, reducing efforts for auto-segmentation pipeline implementation. Furthermore, while deep learning model inference is independent of the deep learning framework, it can easily be integrated into famous deep learning frameworks such as PyTorch or Tensorflow. The developed package has successfully demonstrated its capabilities in a research project at our institution for organs-at-risk segmentation in brain tumor patients. Furthermore, PyRaDiSe has shown its conversion performance for dataset construction. CONCLUSIONS The PyRaDiSe package closes the gap between data science and clinical radiotherapy by enabling deep learning segmentation models to be easily transferred into clinical research practice. PyRaDiSe is available on https://github.com/ubern-mia/pyradise and can be installed directly from the Python Package Index using pip install pyradise

The predictive value of segmentation metrics on dosimetry in organs at risk of the brain.

BACKGROUND Fully automatic medical image segmentation has been a long pursuit in radiotherapy (RT). Recent developments involving deep learning show promising results yielding consistent and time efficient contours. In order to train and validate these systems, several geometric based metrics, such as Dice Similarity Coefficient (DSC), Hausdorff, and other related metrics are currently the standard in automated medical image segmentation challenges. However, the relevance of these metrics in RT is questionable. The quality of automated segmentation results needs to reflect clinical relevant treatment outcomes, such as dosimetry and related tumor control and toxicity. In this study, we present results investigating the correlation between popular geometric segmentation metrics and dose parameters for Organs-At-Risk (OAR) in brain tumor patients, and investigate properties that might be predictive for dose changes in brain radiotherapy. METHODS A retrospective database of glioblastoma multiforme patients was stratified for planning difficulty, from which 12 cases were selected and reference sets of OARs and radiation targets were defined. In order to assess the relation between segmentation quality -as measured by standard segmentation assessment metrics- and quality of RT plans, clinically realistic, yet alternative contours for each OAR of the selected cases were obtained through three methods: (i) Manual contours by two additional human raters. (ii) Realistic manual manipulations of reference contours. (iii) Through deep learning based segmentation results. On the reference structure set a reference plan was generated that was re-optimized for each corresponding alternative contour set. The correlation between segmentation metrics, and dosimetric changes was obtained and analyzed for each OAR, by means of the mean dose and maximum dose to 1% of the volume (Dmax 1%). Furthermore, we conducted specific experiments to investigate the dosimetric effect of alternative OAR contours with respect to the proximity to the target, size, particular shape and relative location to the target. RESULTS We found a low correlation between the DSC, reflecting the alternative OAR contours, and dosimetric changes. The Pearson correlation coefficient between the mean OAR dose effect and the Dice was -0.11. For Dmax 1%, we found a correlation of -0.13. Similar low correlations were found for 22 other segmentation metrics. The organ based analysis showed that there is a better correlation for the larger OARs (i.e. brainstem and eyes) as for the smaller OARs (i.e. optic nerves and chiasm). Furthermore, we found that proximity to the target does not make contour variations more susceptible to the dose effect. However, the direction of the contour variation with respect to the relative location of the target seems to have a strong correlation with the dose effect. CONCLUSIONS This study shows a low correlation between segmentation metrics and dosimetric changes for OARs in brain tumor patients. Results suggest that the current metrics for image segmentation in RT, as well as deep learning systems employing such metrics, need to be revisited towards clinically oriented metrics that better reflect how segmentation quality affects dose distribution and related tumor control and toxicity

Financial Consequences of Ill Health and Informal Coping Mechanisms in Indonesia

We assess the financial risk of ill health for households in Indonesia, the role of informal coping strategies, and the effectiveness of these strategies in smoothing consumption. based on household panel data, we find evidence of financial risk from illness through medical expenses, while income from informal wage labor is exposed to risk for the poor and income from self-employed business activities for the non-poor. however, only for the rural population and the poor does this lead to imperfect consumption smoothing, while the non-poor seem to be able to protect current spending. borrowing and drawing on buffers, such as savings and assets, seem to be key informal coping strategies for the poor, which infers potential negative long term effects. while these results suggest scope for public intervention, the financial risk from income loss for the rural poor is beyond public health care financing reforms. rather, formal sector employment seems to be a key instrument for financial protection from illness, by also reducing income risk. key words: illness, income, consumption smoothing, coping strategies, Indonesia jel: o15, i1

Deep-Learning-Based Dose Predictor for Glioblastoma-Assessing the Sensitivity and Robustness for Dose Awareness in Contouring

External beam radiation therapy requires a sophisticated and laborious planning procedure. To improve the efficiency and quality of this procedure, machine-learning models that predict these dose distributions were introduced. The most recent dose prediction models are based on deep-learning architectures called 3D U-Nets that give good approximations of the dose in 3D almost instantly. Our purpose was to train such a 3D dose prediction model for glioblastoma VMAT treatment and test its robustness and sensitivity for the purpose of quality assurance of automatic contouring. From a cohort of 125 glioblastoma (GBM) patients, VMAT plans were created according to a clinical protocol. The initial model was trained on a cascaded 3D U-Net. A total of 60 cases were used for training, 15 for validation and 20 for testing. The prediction model was tested for sensitivity to dose changes when subject to realistic contour variations. Additionally, the model was tested for robustness by exposing it to a worst-case test set containing out-of-distribution cases. The initially trained prediction model had a dose score of 0.94 Gy and a mean DVH (dose volume histograms) score for all structures of 1.95 Gy. In terms of sensitivity, the model was able to predict the dose changes that occurred due to the contour variations with a mean error of 1.38 Gy. We obtained a 3D VMAT dose prediction model for GBM with limited data, providing good sensitivity to realistic contour variations. We tested and improved the model's robustness by targeted updates to the training set, making it a useful technique for introducing dose awareness in the contouring evaluation and quality assurance process

Impact of random outliers in auto-segmented targets on radiotherapy treatment plans for glioblastoma.

AIMS To save time and have more consistent contours, fully automatic segmentation of targets and organs at risk (OAR) is a valuable asset in radiotherapy. Though current deep learning (DL) based models are on par with manual contouring, they are not perfect and typical errors, as false positives, occur frequently and unpredictably. While it is possible to solve this for OARs, it is far from straightforward for target structures. In order to tackle this problem, in this study, we analyzed the occurrence and the possible dose effects of automated delineation outliers. METHODS First, a set of controlled experiments on synthetically generated outliers on the CT of a glioblastoma (GBM) patient was performed. We analyzed the dosimetric impact on outliers with different location, shape, absolute size and relative size to the main target, resulting in 61 simulated scenarios. Second, multiple segmentation models where trained on a U-Net network based on 80 training sets consisting of GBM cases with annotated gross tumor volume (GTV) and edema structures. On 20 test cases, 5 different trained models and a majority voting method were used to predict the GTV and edema. The amount of outliers on the predictions were determined, as well as their size and distance from the actual target. RESULTS We found that plans containing outliers result in an increased dose to healthy brain tissue. The extent of the dose effect is dependent on the relative size, location and the distance to the main targets and involved OARs. Generally, the larger the absolute outlier volume and the distance to the target the higher the potential dose effect. For 120 predicted GTV and edema structures, we found 1887 outliers. After construction of the planning treatment volume (PTV), 137 outliers remained with a mean distance to the target of 38.5 ± 5.0 mm and a mean size of 1010.8 ± 95.6 mm3. We also found that majority voting of DL results is capable to reduce outliers. CONCLUSIONS This study shows that there is a severe risk of false positive outliers in current DL predictions of target structures. Additionally, these errors will have an evident detrimental impact on the dose and therefore could affect treatment outcome

Deep-Learning-Based Dose Predictor for Glioblastoma–Assessing the Sensitivity and Robustness for Dose Awareness in Contouring

External beam radiation therapy requires a sophisticated and laborious planning procedure. To improve the efficiency and quality of this procedure, machine-learning models that predict these dose distributions were introduced. The most recent dose prediction models are based on deep-learning architectures called 3D U-Nets that give good approximations of the dose in 3D almost instantly. Our purpose was to train such a 3D dose prediction model for glioblastoma VMAT treatment and test its robustness and sensitivity for the purpose of quality assurance of automatic contouring. From a cohort of 125 glioblastoma (GBM) patients, VMAT plans were created according to a clinical protocol. The initial model was trained on a cascaded 3D U-Net. A total of 60 cases were used for training, 15 for validation and 20 for testing. The prediction model was tested for sensitivity to dose changes when subject to realistic contour variations. Additionally, the model was tested for robustness by exposing it to a worst-case test set containing out-of-distribution cases. The initially trained prediction model had a dose score of 0.94 Gy and a mean DVH (dose volume histograms) score for all structures of 1.95 Gy. In terms of sensitivity, the model was able to predict the dose changes that occurred due to the contour variations with a mean error of 1.38 Gy. We obtained a 3D VMAT dose prediction model for GBM with limited data, providing good sensitivity to realistic contour variations. We tested and improved the model’s robustness by targeted updates to the training set, making it a useful technique for introducing dose awareness in the contouring evaluation and quality assurance process

Feasibility of postoperative spine stereotactic body radiation therapy in proximity of carbon and titanium hybrid implants using a robotic radiotherapy device.

BACKGROUND AND PURPOSE To assess the feasibility of postoperative stereotactic body radiation therapy (SBRT) for patients with hybrid implants consisting of carbon fiber reinforced polyetheretherketone and titanium (CFP-T) using CyberKnife. MATERIALS AND METHODS All essential steps within a radiation therapy (RT) workflow were evaluated. First, the contouring process of target volumes and organs at risk (OAR) was done for patients with CFP-T implants. Second, after RT-planning, the accuracy of the calculated dose distributions was tested in a slab phantom and an anthropomorphic phantom using film dosimetry. As a third step, the accuracy of the mandatory image guided radiation therapy (IGRT) including automatic matching was assessed using the anthropomorphic phantom. For this goal, a standard quality assurance (QA) test was modified to carry out its IGRT part in presence of CFP-T implants. RESULTS Using CFP-T implants, target volumes could precisely delineated. There was no need for compromising the contours to overcome artifact obstacles. Differences between measured and calculated dose values were below 11% for the slab phantom, and at least 95% of the voxels were within 5% dose difference. The comparisons for the anthropomorphic phantom showed a gamma-passing rate (5%, 1 mm) of at least 97%. Additionally the test results with and without CFP-T implants were comparable. No issues concerning the IGRT were detected. The modified machine QA test resulted in a targeting error of 0.71 mm, which corresponds to the results of the unmodified standard tests. CONCLUSION Dose calculation and delivery of postoperative spine SBRT is feasible in proximity of CFP-T implants using a CyberKnife system