The COVID-19 pandemic has shifted oncology practices to prioritize patient safety while maintaining necessary treatment delivery. We obtained patient feedback on pandemic-based practices in our radiotherapy department to improve quality of patient care and amend policies as needed. We developed a piloted questionnaire which quantitatively and qualitatively assessed patients’ pandemic-related concerns and satisfaction with specific elements of their care. Adult patients who were treated at our Centre between 23 March and 31 May 2020, had initial consultation via telemedicine, and received at least five outpatient fractions of radiotherapy were invited to complete the survey by telephone or online. Relative frequencies of categorical and ordinal responses were then calculated. Fifty-three (48%) out of 110 eligible patients responded: 32 patients by phone and 21 patients online. Eighteen participants (34%) admitted to feeling anxious about hospital appointments, and only five (9%) reported treatment delays. Forty-eight patients (91%) reported satisfaction with their initial telemedicine appointment. The majority of patients indicated that healthcare workers took appropriate precautions, making them feel safe. Overall, all 53 patients (100%) reported being satisfied with their treatment experience during the pandemic. Patient feedback is needed to provide the highest quality of patient care as we adapt to the current reality.

Radiomics-based machine learning classifiers have shown potential for detecting bone metastases (BM) and for evaluating BM response to radiotherapy (RT). However, current radiomics models require large datasets of images with expert-segmented 3D regions of interest (ROIs). Full ROI segmentation is time consuming and oncologists often outline just RT treatment fields in clinical practice. This presents a challenge for real-world radiomics research. As such, a method that simplifies BM identification but does not compromise the power of radiomics is needed. The objective of this study was to investigate the feasibility of radiomics models for BM detection using lesion-center-based geometric ROIs. The planning-CT images of 170 patients with non-metastatic lung cancer and 189 patients with spinal BM were used. The point locations of 631 BM and 674 healthy bone (HB) regions were identified by experts. ROIs with various geometric shapes were centered and automatically delineated on the identified locations, and 107 radiomics features were extracted. Various feature selection methods and machine learning classifiers were evaluated. Our point-based radiomics pipeline was successful in differentiating BM from HB. Lesion-center-based segmentation approach greatly simplifies the process of preparing images for use in radiomics studies and avoids the bottleneck of full ROI segmentation.

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Background Dose‐outcome studies in radiation oncology have historically excluded spatial information due to dose‐volume histograms being the most dominant source of dosimetric information. In recent years, dose‐surface maps (DSMs) have become increasingly popular for characterization of spatial dose distributions and identification of radiosensitive subregions for hollow organs. However, methodological variations and lack of open‐source, publicly offered code‐sharing between research groups have limited reproducibility and wider adoption. Purpose This paper presents rtdsm, an open‐source software for DSM calculation with the intent to improve the reproducibility of and the access to DSM‐based research in medical physics and radiation oncology. Methods A literature review was conducted to identify essential functionalities and prevailing calculation approaches to guide development. The described software has been designed to calculate DSMs from DICOM data with a high degree of user customizability and to facilitate DSM feature analysis. Core functionalities include DSM calculation, equivalent dose conversions, common DSM feature extraction, and simple DSM accumulation. Results A number of use cases were used to qualitatively and quantitatively demonstrate the use and usefulness of rtdsm. Specifically, two DSM slicing methods, planar and noncoplanar, were implemented and tested, and the effects of method choice on output DSMs were demonstrated. An example comparison of DSMs from two different treatments was used to highlight the use cases of various built‐in analysis functions for equivalent dose conversion and DSM feature extraction. Conclusions We developed and implemented rtdsm as a standalone software that provides all essential functionalities required to perform a DSM‐based study. It has been made freely accessible under an open‐source license on Github to encourage collaboration and community use.