Practical Guidelines on Implementing Hypofractionated Radiotherapy for Prostate Cancer in Africa

Among a growing body of literature in global oncology, several articles project increased cost savings and radiotherapy access by adopting hypofractionated radiotherapy (HFRT) in low- and middle-income countries (LMICs) like those in Africa. Clinical trials in Europe and the USA have demonstrated HFRT to be non-inferior to conventional radiotherapy for eligible patients with several cancers, including prostate cancer. This could be a highly recommended option to battle a severely large and growing cancer burden in resource-limited regions. However, a level of implementation research may be needed in limited resource-settings like in Africa. In this article, we present a list of evidence-based recommendations to practice HFRT on eligible prostate cancer patients. As literature on HFRT is still developing, these guidelines were compiled from review of several clinical trials and professionally accredited material with minimal resource requirements in mind. HFRT guidelines presented here include patient eligibility, prescription dose schedules, treatment planning and delivery techniques, and quality assurance procedures. The article provides recommendations for both moderately hypofractionated (2.4-3.4Gy per fraction) and ultrahypofractionated (5Gy or more per fraction) radiation therapy when administered by 3D-Conformal Radiotherapy, Intensity Modulated Radiation Therapy, or Image-Guided Radiotherapy. In each case radiation oncology health professionals must make the ultimate judgment to ensure safety as more LMIC centers adopt HFRT to combat the growing scourge of cancer.

Radiopaque drug-eluting embolisation beads as fiducial markers for stereotactic liver radiotherapy

Objective: To determine the feasibility of using radiopaque (RO) beads as direct tumour surrogates for image-guided radiotherapy (IGRT) in patients with liver tumours after transarterial chemoembolisation (TACE). Methods: A novel vandetanib-eluting RO bead was delivered via TACE as part of a first-in-human clinical trial in patients with either hepatocellular carcinoma or liver metastases from colorectal cancer. Following TACE, patients underwent simulated radiotherapy imaging with 4-dimensional computed tomography (4D-CT) and cone-beam CT (CBCT) imaging. RO beads were contoured using automated thresholding, and feasibility of matching between the simulated radiotherapy planning dataset (AVE-IP image from 4D data) and CBCT scans assessed. Additional kV, MV, helical CT and CBCT images of RO beads were obtained using an in-house phantom. Stability of RO bead position was assessed by comparing 4D-CT imaging to CT scans taken 6–20 days following TACE. Results: Eight patients were treated and 4D-CT and CBCT images acquired. RO beads were visible on 4D-CT and CBCT images in all cases and matching successfully performed. Differences in centre of mass of RO beads between CBCT and simulated radiotherapy planning scans (AVE-IP dataset) were: 2.0 mm mediolaterally, 1.7 mm anteroposteriorally, 3.5 mm craniocaudally. RO beads in the phantom were visible on all imaging modalities assessed. RO bead position remained stable up to 29 days post-TACE. Conclusion: RO beads are visible on IGRT imaging modalities, showing minimal artefact. They can be used for on-set matching with CBCT and remain stable over time. Advances in knowledge: The role of RO beads as fiducial markers for stereotactic liver radiotherapy is feasible and warrants further exploration as a combination therapy approach.

A feasibility study on the development and use of a deep learning model to automate real-time monitoring of tumor position and assessment of interfraction fiducial marker migration in prostate radiotherapy patients

Purpose The aim of this study was to assess the feasibility of the development and training of a deep learning object detection model for automating the assessment of fiducial marker migration and tracking of the prostate in radiotherapy patients. Methods and Materials A fiducial marker detection model was trained on the YOLO v2 detection framework using approximately 20,000 pelvis kV projection images with fiducial markers labelled. The ability of the trained model to detect marker positions was validated by tracking the motion of markers in a respiratory phantom and comparing detection data with the expected displacement from a reference position. Marker migration was then assessed in 14 prostate radiotherapy patients using the detector for comparison with previously conducted studies. This was done by determining variations in intermarker distance between the first and subsequent fractions in each patient. Results On completion of training, a detection model was developed that operated at a 96% detection efficacy and with a root mean square error of 0.3 pixels. By determining the displacement from a reference position in a respiratory phantom, experimentally and with the detector it was found that the detector was able to compute displacements with a mean accuracy of 97.8% when compared to the actual values. Interfraction marker migration was measured in 14 patients and the average and maximum ± standard deviation marker migration were found to be 2.0±0.9 mm and 2.3±0.9 mm, respectively. Conclusion This study demonstrates the benefits of pairing deep learning object detection, and image-guided radiotherapy and how a workflow to automate the assessment of organ motion and seed migration during prostate radiotherapy can be developed. The high detection efficacy and low error make the advantages of using a pre-trained model to automate the assessment of the target volume positional variation and the migration of fiducial markers between fractions.