Scalable radiotherapy data curation infrastructure for deep-learning based autosegmentation of organs-at-risk: A case study in head and neck cancer.

In this era of patient-centered, outcomes-driven and adaptive radiotherapy, deep learning is now being successfully applied to tackle imaging-related workflow bottlenecks such as autosegmentation and dose planning. These applications typically require supervised learning approaches enabled by relatively large, curated radiotherapy datasets which are highly reflective of the contemporary standard of care. However, little has been previously published describing technical infrastructure, recommendations, methods or standards for radiotherapy dataset curation in a holistic fashion. Our radiation oncology department has recently embarked on a large-scale project in partnership with an external partner to develop deep-learning-based tools to assist with our radiotherapy workflow, beginning with autosegmentation of organs-at-risk. This project will require thousands of carefully curated radiotherapy datasets comprising all body sites we routinely treat with radiotherapy. Given such a large project scope, we have approached the need for dataset curation rigorously, with an aim towards building infrastructure that is compatible with efficiency, automation and scalability. Focusing on our first use-case pertaining to head and neck cancer, we describe our developed infrastructure and novel methods applied to radiotherapy dataset curation, inclusive of personnel and workflow organization, dataset selection, expert organ-at-risk segmentation, quality assurance, patient de-identification, data archival and transfer. Over the course of approximately 13 months, our expert multidisciplinary team generated 490 curated head and neck radiotherapy datasets. This task required approximately 6000 human-expert hours in total (not including planning and infrastructure development time). This infrastructure continues to evolve and will support ongoing and future project efforts.

Volumetric modulated arc therapy for hippocampal-sparing radiotherapy in transformed low-grade glioma: A treatment planning case report.

Timing of radiotherapy for low-grade gliomas is still controversial due to concerns of possible adverse late effects. Prevention of possible late cognitive sequelae by hippocampal avoidance has shown promise in phase II trials. A patient with progressive low-grade glioma with gradual dedifferentiation into anaplastic astrocytoma is presented along with description of radiotherapy planning process attempting to spare the hippocampus. To our knowledge, this is the first described case using volumetric modulated arc technique to spare hippocampus during transformed low-grade glioma radiotherapy. Using modern intensity-modulated radiotherapy systems it is possible to selectively spare hippocampus together with other standard organs at risk. For selected patients, an attempt to spare hippocampus can be considered as long as other dose characteristics are not significantly compromised compared to standard treatment plan created without any effort to avoid hippocampus.