Hester Burger

Pediatric Radiotherapy in Low and Middle Income Countries

Communicable diseases are still the leading cause of childhood mortality in third world countries. However, as prevention and treatment of malnutrition and infectious diseases improves, global health challenges are shifting towards combating non-communicable diseases including cancer which has high rates of mortality in children (Wilimas and Ribeiro 2001). More than two-thirds of the world’s pediatric cancers are currently diagnosed in low- and middle income countries (LMIC) (Kellie and Howard 2008). The patterns of occurrence of childhood cancer in LMIC compared to high income countries (HIC) and the lack of population-based cancer registries suggest that many patients die from undiagnosed cancer and the burden of childhood cancer is under-estimated. Children diagnosed with cancer in low-income countries (LIC) continue to have a much poorer chance of survival compared to those in HIC (Fig. 21.1).

Radiation planning assistant - A streamlined, fully automated radiotherapy treatment planning system

The Radiation Planning Assistant (RPA) is a system developed for the fully automated creation of radiotherapy treatment plans, including volume-modulated arc therapy (VMAT) plans for patients with head/neck cancer and 4-field box plans for patients with cervical cancer. It is a combination of specially developed in-house software that uses an application programming interface to communicate with a commercial radiotherapy treatment planning system. It also interfaces with a commercial secondary dose verification software. The necessary inputs to the system are a Treatment Plan Order, approved by the radiation oncologist, and a simulation computed tomography (CT) image, approved by the radiographer. The RPA then generates a complete radiotherapy treatment plan. For the cervical cancer treatment plans, no additional user intervention is necessary until the plan is complete. For head/neck treatment plans, after the normal tissue and some of the target structures are automatically delineated on the CT image, the radiation oncologist must review the contours, making edits if necessary. They also delineate the gross tumor volume. The RPA then completes the treatment planning process, creating a VMAT plan. Finally, the completed plan must be reviewed by qualified clinical staff.

Retrospective Validation and Clinical Implementation of Automated Contouring of Organs at Risk in the Head and Neck: A Step Toward Automated Radiation Treatment Planning for Low- and Middle-Income Countries

Purpose We assessed automated contouring of normal structures for patients with head-and-neck cancer (HNC) using a multiatlas deformable-image-registration algorithm to better provide a fully automated radiation treatment planning solution for low- and middle-income countries, provide quantitative analysis, and determine acceptability worldwide. Methods Autocontours of eight normal structures (brain, brainstem, cochleae, eyes, lungs, mandible, parotid glands, and spinal cord) from 128 patients with HNC were retrospectively scored by a dedicated HNC radiation oncologist. Contours from a 10-patient subset were evaluated by five additional radiation oncologists from international partner institutions, and interphysician variability was assessed. Quantitative agreement of autocontours with independently physician-drawn structures was assessed using the Dice similarity coefficient and mean surface and Hausdorff distances. Automated contouring was then implemented clinically and has been used for 166 patients, and contours were quantitatively compared with the physician-edited autocontours using the same metrics. Results Retrospectively, 87% of normal structure contours were rated as acceptable for use in dose-volume-histogram–based planning without edit. Upon clinical implementation, 50% of contours were not edited for use in treatment planning. The mean (± standard deviation) Dice similarity coefficient of autocontours compared with physician-edited autocontours for parotid glands (0.92 ± 0.10), brainstem (0.95 ± 0.09), and spinal cord (0.92 ± 0.12) indicate that only minor edits were performed. The average mean surface and Hausdorff distances for all structures were less than 0.15 mm and 1.8 mm, respectively. Conclusion Automated contouring of normal structures generates reliable contours that require only minimal editing, as judged by retrospective ratings from multiple international centers and clinical integration. Autocontours are acceptable for treatment planning with no or, at most, minor edits, suggesting that automated contouring is feasible for clinical use and in the ongoing development of automated radiation treatment planning algorithms.

SIOP PODC Adapted treatment guidelines for low grade gliomas in low and middle income settings

Effective treatment of children with low grade glioma (LGG) requires a functioning multi‐disciplinary team with adequate neurosurgical, neuroradiological, pathological, radiotherapy and chemotherapy facilities and personnel. In addition, the treating centre should have the capacity to manage a variety of LGG and treatment‐associated complications. These requirements have made it difficult for many centers in low and middle‐income countries (LMIC) to offer effective treatment and follow up. This article provides management recommendations for children with LGG according to the level of facilities available.