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PurposeThe ongoing lack of data standardization severely undermines the potential for automated learning from the vast amount of information routinely archived in electronic health records (EHRs), Radiation Oncology Information Systems (ROIS), treatment planning systems (TPSs), and other cancer care and outcomes databases. The effort sought to create a standardized ontology for clinical data, social determinants of health (SDOH), and other radiation oncology concepts and interrelationships.Methods and materialsThe American Association of Physicists in Medicine's (AAPM's) Big Data Science Committee (BDSC) was initiated July of 2019 to explore common ground from the stakeholders' collective experience of issues that typically compromise the formation of large inter- and intra- institutional databases from EHRs. The BDSC adopted an iterative, cyclical approach to engaging stakeholders beyond its membership to optimize the integration of diverse perspectives from the community.ResultsWe developed the Operational Ontology for Oncology (O3) which identified 42 key elements, 359 attributes, 144 value sets, and 155 relationships ranked in relative importance of clinical significance, likelihood of availability in EHRs, or the ability to modify routine clinical processes to permit aggregation. Recommendations are provided for best use and development of the O3 to four constituencies: device manufacturers, centers of clinical care, researchers, and professional societies.ConclusionsO3 is designed to extend and interoperate with existing global infrastructure and data science standards. The implementation of these recommendations will lower the barriers for aggregation of information that could be used creating large, representative, findable, accessible, interoperable and reusable (FAIR) datasets supporting the scientific objectives of grant programs. The construction of comprehensive "real world" datasets and application of advanced analytic techniques, including artificial intelligence (AI), holds the potential to revolutionize patient management and improve outcomes by leveraging increased access to information derived from larger, more representative datasets.

Abstract Objective Proton therapy can result in clinically significant radiation dermatitis. In some clinical scenarios, such as lung or breast cancer, the risk of severe radiation dermatitis may limit beam arrangement and prescription doses. Patients undergoing proton therapy for prostate cancer commonly develop mild radiation dermatitis. Herein, we report the outcomes of two prostate cancer patients whose radiation dermatitis appears to have been substantially diminished by transparent film dressings (Beekley stickers). Methods This is a descriptive report of the skin toxicity observed in two patients undergoing proton therapy for prostate cancer at a single institution in 2011. A phantom dosimetric study was performed to evaluate the impact of a transparent film dressing on a beam’s spread out Bragg peak (SOBP). Results Two patients with low risk prostate cancer were treated with proton therapy to a total dose of 79.2Gy (RBE) in 1.8 Gy (RBE) fractions using two opposed lateral beams daily. Both patients had small circular (2.5 cm diameter) transparent adhesive markers placed on their skin to assist with daily alignment. Patient 1 had markers in place bilaterally for the entirety of treatment. Patient 2 had a marker in place for three weeks on one side and six weeks on the other. Over the course of therapy, both men developed typical Grade 1 radiation dermatitis (asymptomatic erythema) on their hips; however, in both patients, the erythema was substantially decreased beneath the markers. Patient 2 demonstrated less attenuation and thus greater erythema in the skin covered for three weeks compared to the skin covered for six weeks. The difference in skin changes between the covered and uncovered skin persisted for at least 1 month. A phantom study of double scattered beam SOBP with and without the marker in the beam path showed no gross dosimetric effect. Conclusions Transparent adhesive markers appear to have attenuated radiation dermatitis in these two patients without affecting the SOBP. One patient may have exhibited a dose–response effect. The reproducibility and underlying mechanisms are unclear. However, the potential to leverage this effect to improve proton-related radiation dermatitis in other clinical scenarios is intriguing. Exploratory animal studies are underway.

Competing Interests: JC is Senior Vice President of Medical Affairs & Clinical Research at Elekta and Adjunct Associate Professor of Radiation Oncology at the Hospital of the University of Pennsylvania. TL is Vice President and Head of Global Policy and Patient Access at Elekta and is a co-founder of Global Coalition for Radiotherapy. GS is President and Chief Executive Officer at Elekta.

Radiation therapy (RT) is a crucial treatment for head and neck squamous cell carcinoma (HNSCC); however, it can have adverse effects on patients' long-term function and quality of life. Biomarkers that can predict tumor response to RT are being explored to personalize treatment and improve outcomes. While tissue and blood biomarkers have limitations, imaging biomarkers derived from magnetic resonance imaging (MRI) offer detailed information. The integration of MRI and a linear accelerator in the MR-Linac system allows for MR-guided radiation therapy (MRgRT), offering precise visualization and treatment delivery. This data descriptor offers a valuable repository for weekly intra-treatment diffusion-weighted imaging (DWI) data obtained from head and neck cancer patients. By analyzing the sequential DWI changes and their correlation with treatment response, as well as oncological and survival outcomes, the study provides valuable insights into the clinical implications of DWI in HNSCC., (© 2024. The Author(s).)

Clinical evidence is crucial in enabling the judicious adoption of technological innovations in radiation therapy (RT). Pharmaceutical evidence development frameworks are not useful for understanding how technical advances are maturing. In this paper, we introduce a new framework, the Radiation Therapy Technology Evidence Matrix (rtTEM), that helps visualize how the clinical evidence supporting new technologies is developing. The matrix is a unique 2D model based on the R-IDEAL clinical evaluation framework. It can be applied to clinical hypothesis testing trials, as well as publications reporting clinical treatment. We present the rtTEM and illustrate its application, using emerging and mature RT technologies as examples. The model breaks down the type of claim along the vertical axis and the strength of the evidence for that claim on the horizontal axis, both of which are inherent in clinical hypothesis testing. This simplified view allows for stakeholders to understand where the evidence is and where it is heading. Ultimately, the value of an innovation is typically demonstrated through superiority studies, which we have divided into three key categories - administrative, toxicity and control, to enable more detailed visibility of evidence development in that claim area. We propose the rtTEM can be used to track evidence development for new interventions in RT. We believe it will enable researchers and sponsors to identify gaps in evidence and to further direct evidence development. Thus, by highlighting evidence looked for by key policy decision makers, the rtTEM will support wider, timely patient access to high value technological advances., Competing Interests: The authors declare that they are all employees of Elekta AB, Stockholm, Sweden., (Copyright © 2024 Edwards, Luzzara, Dell’Acqua and Christodouleas.)

Background and Purpose: This study assessed quality of life (QoL) and clinical outcomes in rectal cancer patients treated with magnetic resonance (MR) guided short-course radiation therapy (SCRT) on a 1.5 Tesla (T) MR-Linac during the first 12 months after treatment., Materials and Methods: Rectal cancer patients treated with 25 Gy SCRT in five fractions with curative intent in the Netherlands (2019-2022) were identified in MOMENTUM (NCT04075305). Toxicity (CTCAE v5) and QoL (EORTC QLQ-C30 and -CR29) was primarily analyzed in patients without metastatic disease (M0) and no other therapies after SCRT. Patients who underwent tumor resection were censored from surgery. A generalized linear mixed-model was used to investigate clinically meaningful (≥10) and significant (P < 0.05) QoL changes. Clinical and pathological complete response (cCR and pCR) rates were calculated in patients in whom response was documented., Results: A total of 172 patients were included, of whom 112 patients were primarily analyzed. Acute and late radiation-induced high-grade toxicity were reported in one patient, respectively. CCR was observed in 8/64 patients (13 %), 14/37 patients (38 %) and 13/16 patients (91 %) at three, six and twelve months; pCR was observed in 3/69 (4 %) patients. After 12 months, diarrhea (mean difference [MD] -17.4 [95 % confidence interval [CI] -31.2 to -3.7]), blood and mucus in stool (MD -31.1 [95 % CI -46.4 to -15.8]), and anxiety (MD -22.4 [95 % CI -34.0 to -10.9]) were improved., Conclusion: High-field MR-guided SCRT for the treatment of patients with rectal cancer is associated with improved disease-related symptom management and functioning one year after treatment., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2024 The Author(s).)

Background: In order to accurately accumulate delivered dose for head and neck cancer patients treated with the Adapt to Position workflow on the 1.5T magnetic resonance imaging (MRI)-linear accelerator (MR-linac), the low-resolution T2-weighted MRIs used for daily setup must be segmented to enable reconstruction of the delivered dose at each fraction., Purpose: In this pilot study, we evaluate various autosegmentation methods for head and neck organs at risk (OARs) on on-board setup MRIs from the MR-linac for off-line reconstruction of delivered dose., Methods: Seven OARs (parotid glands, submandibular glands, mandible, spinal cord, and brainstem) were contoured on 43 images by seven observers each. Ground truth contours were generated using a simultaneous truth and performance level estimation (STAPLE) algorithm. Twenty total autosegmentation methods were evaluated in ADMIRE: 1-9) atlas-based autosegmentation using a population atlas library (PAL) of 5/10/15 patients with STAPLE, patch fusion (PF), random forest (RF) for label fusion; 10-19) autosegmentation using images from a patient's 1-4 prior fractions (individualized patient prior [IPP]) using STAPLE/PF/RF; 20) deep learning (DL) (3D ResUNet trained on 43 ground truth structure sets plus 45 contoured by one observer). Execution time was measured for each method. Autosegmented structures were compared to ground truth structures using the Dice similarity coefficient, mean surface distance (MSD), Hausdorff distance (HD), and Jaccard index (JI). For each metric and OAR, performance was compared to the inter-observer variability using Dunn's test with control. Methods were compared pairwise using the Steel-Dwass test for each metric pooled across all OARs. Further dosimetric analysis was performed on three high-performing autosegmentation methods (DL, IPP with RF and 4 fractions [IPP&#95;RF&#95;4], IPP with 1 fraction [IPP&#95;1]), and one low-performing (PAL with STAPLE and 5 atlases [PAL&#95;ST&#95;5]). For five patients, delivered doses from clinical plans were recalculated on setup images with ground truth and autosegmented structure sets. Differences in maximum and mean dose to each structure between the ground truth and autosegmented structures were calculated and correlated with geometric metrics., Results: DL and IPP methods performed best overall, all significantly outperforming inter-observer variability and with no significant difference between methods in pairwise comparison. PAL methods performed worst overall; most were not significantly different from the inter-observer variability or from each other. DL was the fastest method (33 s per case) and PAL methods the slowest (3.7-13.8 min per case). Execution time increased with a number of prior fractions/atlases for IPP and PAL. For DL, IPP&#95;1, and IPP&#95;RF&#95;4, the majority (95%) of dose differences were within ± 250 cGy from ground truth, but outlier differences up to 785 cGy occurred. Dose differences were much higher for PAL&#95;ST&#95;5, with outlier differences up to 1920 cGy. Dose differences showed weak but significant correlations with all geometric metrics (R2 between 0.030 and 0.314)., Conclusions: The autosegmentation methods offering the best combination of performance and execution time are DL and IPP&#95;1. Dose reconstruction on on-board T2-weighted MRIs is feasible with autosegmented structures with minimal dosimetric variation from ground truth, but contours should be visually inspected prior to dose reconstruction in an end-to-end dose accumulation workflow., (© 2023 The Authors. Medical Physics published by Wiley Periodicals LLC on behalf of American Association of Physicists in Medicine.)