Tanja Alderliesten, PhD, is associate professor at the department of Radiation Oncology of the Leiden University Medical Center (LUMC) in the Netherlands. She studied Medical Computer Science at Utrecht University and ever since has a keen interest to innovate healthcare by bridging the gap between fundamental computer science and mathematics research and healthcare applications. She performed her PhD research at the Image Sciences Institute (ISI), an interdisciplinary research institute located in the University Medical Center Utrecht, after which she worked as a postdoctoral researcher at the Netherlands Cancer Institute – Antoni van Leeuwenhoek hospital (NKI-AVL) at the departments of Radiology and Radiation Oncology. She subsequently worked as a tenured senior researcher at the department of Radiation Oncology of the Amsterdam UMC, location Academic Medical Center, where she continued to lay the foundation for her current research activities focusing on image processing, optimization of radiation treatment planning, and explainable artificial intelligence (AI). In 2019, she joined the LUMC, where she currently is leading the AI-based innovations research group that aims to innovate the treatment of cancer by 1) tailoring AI to innovate tasks that are part of the image-guided radiation treatment workflow (including the identification and deformable registration of relevant regions in medical images and the optimization of radiation treatment plans), and 2) enabling better informed and improved clinical decision support to, for example, patient-specifically tailor the follow-up procedure and choice of preferred treatment.
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Performing large-scale three-dimensional radiation dose reconstruction for patients requires a large amount of manual work. We present an image processing-based pipeline to automatically reconstruct radiation dose. The pipeline was designed for childhood cancer survivors that received abdominal radiotherapy with anterior-to-posterior and posterior-to-anterior field set-up. First, anatomical landmarks are automatically identified on two-dimensional radiographs. Second, these landmarks are used to derive parameters to emulate the geometry of the plan on a surrogate computed tomography. Finally, the plan is emulated and used as input for dose calculation. For qualitative evaluation, 100 cases of automatic and manual plan emulations were assessed by two experienced radiation dosimetrists in a blinded comparison. The two radiation dosimetrists approved 100%/100% and 92%/91% of the automatic/manual plan emulations, respectively. Similar approval rates of 100% and 94% hold when the automatic pipeline is applied on another 50 cases. Further, quantitative comparisons resulted in on average <5 mm difference in plan isocenter/borders, and <0.9 Gy in organ mean dose (prescribed dose: 14.4 Gy) calculated from the automatic and manual plan emulations. No statistically significant difference in terms of dose reconstruction accuracy was found for most organs at risk. Ultimately, our automatic pipeline results are of sufficient quality to enable effortless scaling of dose reconstruction data generation.
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