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Intensity-modulated radiotherapy and image-guided radiotherapy allow us to deliver radiation precisely to the tumor and conform the dose distribution to complex shaped target volumes. Nowadays, the largest uncertainty is often the definition of the target volume, i.e. the region that should be irradiated. Radiotherapy planning distinguishes three target volumes:
GTV definition improved with the development of novel biomedical imaging techniques such as novel PET tracers and functional MRI. PTV margins were reduced through modern image guidance. Arguably, the definition of the CTV has made the least progress over the years. GTV definition amounts to delineating an abnormal tumor mass that es visible on imaging. In contrast, the CTV is invisible as it consists of microscopic tumor infiltration into normal appearing functioning tissues. Therefore, CTV definition is conceptually different from GTV definiton and is based on knowledge of the anatomically defined routes of tumor progression.
CTV definition has long been considered the domain of radiation oncologists and has attracted relatively little attention in the medical physics community. In our research group we work on computational methods to support target volume definition. Current research focuses on lymphatic cancer progression; past research projects considered brain tumors. This publication summarizes the role of computational methods for improving CTV definition:
This presentation given remotely at the annual ESTRO meeting 2020 provides an overview on how computational methods may improve or automate the definition of the clinical target volume (CTV).
Head & neck squamous cell carcinoma (HNSCC) spread through the lymphatic system and form metastases in regional lymph nodes. Target volume definition involves detecting macroscopic metastases based on CT, PET, and MR imaging. However, in current practice a much larger part of the lymph drainage region is irradiated, which may harbor microscopic metastases despite negative imaging findings. In one of our projects, we work on statistical models for the lymphatic progression of tumors using Bayesian networks and Hidden Markov Models:
Two videos provide an introduction to our work, which is funded by the clinical reserach priority program (CRPP) on Artificial Intelligence in Oncological Imaging by the UZH.
1) Introduction to AI for target volume definition in head & neck cancer
2) Modeling lymphatic progression of cancer
As part of our efforts, we develop the online platformLyProx to host, share and visualize datasets on patterns of lymph node involvement in HNSCC patients, which is the basis for a better quantification of tumor progression. The GUI allows users to study how the risk of lymph node metastases in a lymph node level depends on location and stage of the primary tumor and the presence of lymph node metastases in other lymph node levels.
Glioblastoma are known to infiltrate the healthy appearing brain tissue far beyond the tumor mass that is visible on MR imaging. However, the spatial patterns of tumor infiltartion are determined by the complex anatomy of the brain. Tumor cells spread primarily along white matter fibers while the falx and the ventricles represent anatomical barriers. In a previous project, we worked on phenomenological tumor growth models to facilitate automatic CTV definition that is consistent with neuroanatomy.
