Reduction of patient specific quality assurance through plan complexity metrics for VMAT plans with an open-source TPS script

PURPOSE Volumetric modulated arc therapy (VMAT) is a widespread technique for the delivery of normo-fractionated radiation therapy (NFRT) and stereotactic body radiation therapy (SBRT). It is associated with a significant hardware burden requiring dose rate modulation, collimator movement and gantry rotation synchronisation. Patient specific quality assurance (PSQA) guarantees that the linacs can precisely and accurately deliver the planned dose. However, PSQA requires a significant time allocation and class solutions to reduce this while guaranteeing the deliverability of the plans should be investigated. METHODS In this study, an in-house developed Eclipse Scripting API (ESAPI) script was used to extract five independent plan complexity metrics from N = 667 VMAT treatment fields. The correlation between metrics and portal dosimetry measurements was investigated with Pearson correlation, box plot analysis and receiver operating characteristic curves, which were used to defined the best performing metric and its threshold. RESULTS The incidence of fields failing the clinical PSQA criteria of 3%/2mm (NFRT) and 3%/1.5mm (SBRT) was low (N = 1). The mean MLC opening was the metric with the highest correlation with the portal dosimetry data and among the best in discriminating the requirement of PSQA. The thresholds of 16.12 mm (NFRT) and 7.96 mm (SBRT) corresponded to true positive rates higher than 90%. CONCLUSIONS This work presents a quantitative approach to reduce the time allocation for PSQA by identifying the most complex plans demanding a dedicated measurement. The proposed method requires PSQA for approximately 10% of the plans. The ESAPI script is distributed open-source to ease the investigation and implementation at other institutions

Adaptive fractionation at the MR-linac

Objective. Fractionated radiotherapy typically delivers the same dose in each fraction. Adaptive fractionation (AF) is an approach to exploit inter-fraction motion by increasing the dose on days when the distance of tumor and dose-limiting organs at risk (OAR) is large and decreasing the dose on unfavorable days. We develop an AF algorithm and evaluate the concept for patients with abdominal tumors previously treated at the MR-linac in 5 fractions.Approach. Given daily adapted treatment plans, inter-fractional changes are quantified by sparing factorsδ$_{t}$defined as the OAR-to-tumor dose ratio. The key problem of AF is to decide on the dose to deliver in fractiont, givenδ$_{t}$and the dose delivered in previous fractions, but not knowing futureδ$_{t}$s. Optimal doses that maximize the expected biologically effective dose in the tumor (BED$_{10}$) while staying below a maximum OAR BED$_{3}$constraint are computed using dynamic programming, assuming a normal distribution overδwith mean and variance estimated from previously observed patient-specificδ$_{t}$s. The algorithm is evaluated for 16 MR-linac patients in whom tumor dose was compromised due to proximity of bowel, stomach, or duodenum.Main Results. In 14 out of the 16 patients, AF increased the tumor BED$_{10}$compared to the reference treatment that delivers the same OAR dose in each fraction. However, in 11 of these 14 patients, the increase in BED$_{10}$was below 1 Gy. Two patients with large sparing factor variation had a benefit of more than 10 Gy BED$_{10}$increase. For one patient, AF led to a 5 Gy BED$_{10}$decrease due to an unfavorable order of sparing factors.Significance. On average, AF provided only a small increase in tumor BED. However, AF may yield substantial benefits for individual patients with large variations in the geometry

A Novel Radiomics-Based Tumor Volume Segmentation Algorithm for Lung Tumors in FDG-PET/CT after 3D Motion Correction-A Technical Feasibility and Stability Study

Positron emission tomography (PET) provides important additional information when applied in radiation therapy treatment planning. However, the optimal way to define tumors in PET images is still undetermined. As radiomics features are gaining more and more importance in PET image interpretation as well, we aimed to use textural features for an optimal differentiation between tumoral tissue and surrounding tissue to segment-target lesions based on three textural parameters found to be suitable in previous analysis (Kurtosis, Local Entropy and Long Zone Emphasis). Intended for use in radiation therapy planning, this algorithm was combined with a previously described motion-correction algorithm and validated in phantom data. In addition, feasibility was shown in five patients. The algorithms provided sufficient results for phantom and patient data. The stability of the results was analyzed in 20 consecutive measurements of phantom data. Results for textural feature-based algorithms were slightly worse than those of the threshold-based reference algorithm (mean standard deviation 1.2%-compared to 4.2% to 8.6%) However, the Entropy-based algorithm came the closest to the real volume of the phantom sphere of 6 ccm with a mean measured volume of 26.5 ccm. The threshold-based algorithm found a mean volume of 25.0 ccm. In conclusion, we showed a novel, radiomics-based tumor segmentation algorithm in FDG-PET with promising results in phantom studies concerning recovered lesion volume and reasonable results in stability in consecutive measurements. Segmentation based on Entropy was the most precise in comparison with sphere volume but showed the worst stability in consecutive measurements. Despite these promising results, further studies with larger patient cohorts and histopathological standards need to be performed for further validation of the presented algorithms and their applicability in clinical routines. In addition, their application in other tumor entities needs to be studied

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

Automatized Self-Supervised Learning for Skin Lesion Screening

The incidence rates of melanoma, the deadliest form of skin cancer, have been increasing steadily worldwide, presenting a significant challenge to dermatologists. Early detection of melanoma is crucial for improving patient survival rates, but identifying suspicious lesions through ugly duckling (UD) screening, the current method used for skin cancer screening, can be challenging and often requires expertise in pigmented lesions. To address these challenges and improve patient outcomes, an artificial intelligence (AI) decision support tool was developed to assist dermatologists in identifying UD from wide-field patient images. The tool uses a state-of-the-art object detection algorithm to identify and extract all skin lesions from patient images, which are then sorted by suspiciousness using a self-supervised AI algorithm. A clinical validation study was conducted to evaluate the tool's performance, which demonstrated an average sensitivity of 93% for the top-10 AI-identified UDs on skin lesions selected by the majority of experts in pigmented skin lesions. The study also found that dermatologists confidence increased, and the average majority agreement with the top-10 AI-identified UDs improved to 100% when assisted by AI. The development of this AI decision support tool aims to address the shortage of specialists, enable at-risk patients to receive faster consultations and understand the impact of AI-assisted screening. The tool's automation can assist dermatologists in identifying suspicious lesions and provide a more objective assessment, reducing subjectivity in the screening process. The future steps for this project include expanding the dataset to include histologically confirmed melanoma cases and increasing the number of participants for clinical validation to strengthen the tool's reliability and adapt it for real-world consultation.Comment: 11 pages, 4 figure

Using endogenous saccades to characterize fatigue in multiple sclerosis

Purpose Multiple Sclerosis (MS) is likely to cause dysfunction of neural circuits between brain regions increasing brain working load or a subjective overestimation of such working load leading to fatigue symptoms. The aim of this study was to investigate if saccades can reveal the effect of fatigue in patients with MS. Methods Patients diagnosed with MS (EDSS<=3) and age matched controls were recruited. Eye movements were monitored using an infrared eyetracker. Each participant performed 40 trials in an endogenous generated saccade paradigm (valid and invalid trials). The fatigue severity scale (FSS) was used to assess the severity of fatigue. FSS scores were used to define two subgroups, the MS fatigue group (score above normal range) and the MS non-fatigue. Differences between groups were tested using linear mixed models. Results Thirty-one MS patients and equal number of controls participated in this study. FSS scores were above the normal range in 11 patients. Differences in saccade latency were found according to group (p<0.001) and trial validity (p=0.023). Differences were 16.9 ms, between MS fatigue and MS non-fatigue, 15.5 ms between MS fatigue and control. The mean difference between valid and invalid trials was 7.5 ms. Differences in saccade peak velocity were found according to group (p<0.001), the difference between MS fatigue and control was 22.3°/s and between MS fatigue and non-fatigue was 12.3°/s. Group was a statistically significant predictor for amplitude (p<0.001). FSS scores were correlated with peak velocity (p=0.028) and amplitude (p=0.019). Conclusion Consistent with the initial hypothesis, our study revealed altered saccade latency, peak velocity and amplitude in patients with fatigue symptoms. Eye movement testing can complement the standard inventories when investigating fatigue because they do not share similar limitations. Our findings contribute to the understanding of functional changes induced by MS and might be useful for clinical trials and treatment decisions.We would like to acknowledge that part of this work has been presented at 3rd International Porto Congress of Multiple Sclerosis, February 27–28, 2015, Porto, Portugal and ECEM 2015 | XVIII. European Conference on Eye Movements, August 16–21, 2015, Viena, Austria. We thank the Multiple Sclerosis Association “Todos com a Esclerose Multiple (TEM)” and the Clinical and Academic Centre (CAA-Hospital de Braga) for their support financial support and for providing facilities for data collection, respectively. We also acknowledge: i) Carla Sofia for recruiting all the MS participants and most of the controls, ii) Two anonymous reviewers for their opinion about an early version of this manuscript and iii) Liz Pearce for proofreading the manuscript. Vision Rehabilitation Lab. receives founding from Shamir Portugal and from grant PTDC/DTP-EPI/0412/2012, Fundação para a Ciência e a Tecnologia, co-financiado pelo FEDER através do COMPETE.info:eu-repo/semantics/publishedVersio

Robustness of radiomic features in magnetic resonance imaging for patients with glioblastoma: Multi-center study

Background and purpose Radiomics offers great potential in improving diagnosis and treatment for patients with glioblastoma multiforme. However, in order to implement radiomics in clinical routine, the features used for prognostic modelling need to be stable. This comprises significant challenge in multi-center studies. The aim of this study was to evaluate the impact of different image normalization methods on MRI features robustness in multi-center study. Methods Radiomics stability was checked on magnetic resonance images of eleven patients. The images were acquired in two different hospitals using contrast-enhanced T1 sequences. The images were normalized using one of five investigated approaches including grey-level discretization, histogram matching and z-score. Then, radiomic features were extracted and features stability was evaluated using intra-class correlation coefficients. In the second part of the study, improvement in the prognostic performance of features was tested on 60 patients derived from publicly available dataset. Results Depending on the normalization scheme, the percentage of stable features varied from 3.4% to 8%. The histogram matching based on the tumor region showed the highest amount of the stable features (113/1404); while normalization using fixed bin size resulted in 48 stable features. The histogram matching also led to better prognostic value (median c-index increase of 0.065) comparing to non-normalized images. Conclusions MRI normalization plays an important role in radiomics. Appropriate normalization helps to select robust features, which can be used for prognostic modelling in multicenter studies. In our study, histogram matching based on tumor region improved both stability of radiomic features and their prognostic value

Corrigendum to "Robustness of radiomic features in magnetic resonance imaging for patients with glioblastoma: Multi-center study" [Phys. Imaging Radiat. Oncol. 22 (2022) 131-136]

Background and purpose Radiomics offers great potential in improving diagnosis and treatment for patients with glioblastoma multiforme. However, in order to implement radiomics in clinical routine, the features used for prognostic modelling need to be stable. This comprises significant challenge in multi-center studies. The aim of this study was to evaluate the impact of different image normalization methods on MRI features robustness in multi-center study. Methods Radiomics stability was checked on magnetic resonance images of eleven patients. The images were acquired in two different hospitals using contrast-enhanced T1 sequences. The images were normalized using one of five investigated approaches including grey-level discretization, histogram matching and z-score. Then, radiomic features were extracted and features stability was evaluated using intra-class correlation coefficients. In the second part of the study, improvement in the prognostic performance of features was tested on 60 patients derived from publicly available dataset. Results Depending on the normalization scheme, the percentage of stable features varied from 3.4% to 8%. The histogram matching based on the tumor region showed the highest amount of the stable features (113/1404); while normalization using fixed bin size resulted in 48 stable features. The histogram matching also led to better prognostic value (median c-index increase of 0.065) comparing to non-normalized images. Conclusions MRI normalization plays an important role in radiomics. Appropriate normalization helps to select robust features, which can be used for prognostic modelling in multicenter studies. In our study, histogram matching based on tumor region improved both stability of radiomic features and their prognostic value

Prospective assessment of stress and health concerns of radiation oncology staff during the COVID-19 pandemic

Introduction and background The COVID-19 pandemic has required rapid and repetitive adjustment of radiotherapy practice, hospital-level and department-level organization and hygiene measures. To prospectively monitor and manage stress levels and health concerns, employees of a radiation oncology department were invited to participate in weekly online surveys during the first year of the pandemic. Materials and methods Starting March 31st, 2020, cross-sectional online surveys were distributed to all employees of the Department of Radiation Oncology, University Hospital Zurich. The survey included questions about the profession, the work setting, the global stress level as well as the health concerns during the past work week. Stress levels and health concerns were assessed on a 10-point scale. SurveyMonkey® was used to conduct the survey. Distribution was performed via email. Participation was anonymous and voluntary. Results Between March 2020 and February 2021, 50 weekly surveys were distributed to 127 employees on average and resulted in 1,877 individual responses. The average response rate was 30%. The mean global stress level varied significantly by profession, ranging from 2.7 (±2.5) points for administrative staff to 6.9 (±2.3) points for radiation therapy technicians (p < 0.001). The mean global stress level was highest with 4.8 (±2.9) points for in-hospital work with direct patient contact. Health concerns were highest regarding family and friends with 4.0 (±3.1) points on average. Changes of the stress level varied in correlation with infection waves. Conclusion Weekly online surveys for prospective assessment of stress levels and health concerns were successfully conducted during the first year of the COVID-19 pandemic, indicating their feasibility and value to monitor profession and workplace specific stress patterns and to allowed for tailored interventions. The physical and mental health of frontline healthcare workers in radiation oncology should remain a top priority for departmental leadership beyond the COVID-19 pandemic

Repeat stereotactic body radiotherapy for oligometastatic disease

BACKGROUND Patients with oligometastatic disease (OMD) treated with metastasis-directed definitive local therapy such as stereotactic body radiotherapy (SBRT) are at risk of developing new metastases. Here, we compare characteristics and outcomes of patients treated with a single course and repeat SBRT. MATERIALS/METHODS OMD patients treated with SBRT to 1-5 metastases were included in this retrospective study, and classified as single course or repeat SBRT. Progression-free survival (PFS), widespread failure-free survival (WFFS), overall survival (OS), systemic therapy-free survival (STFS) and cumulative incidence of different first failures were analyzed. Patient and treatment characteristics predicting the use of repeat SBRT were investigated using univariable and multivariable logistic regression. RESULTS Among the 385 patients included, 129 and 256 received repeat or single course SBRT, respectively. The most common primary tumor and OMD state in both groups were lung cancer and metachronous oligorecurrence. Patients treated with repeat SBRT had shorter PFS (p < 0.0001), while WFFS (p = 0.47) and STFS (p = 0.22) were comparable. Distant failure, particularly with a single metastasis, was more frequently observed in repeat SBRT patients. Repeat SBRT patients had longer median OS (p = 0.01). On multivariable logistic regression, low distant metastases velocity and more previous lines of systemic therapy significantly predicted the use of repeat SBRT. CONCLUSION Despite shorter PFS and comparable WFFS and STFS, repeat SBRT patients had longer OS. The role of repeat SBRT for OMD patients warrants further prospective investigation, focussing on predictive factors to select patients that might derive a benefit