Analysis of lymphatic metastasis and progression patterns for clinical target volume (CTV) definition in head and neck squamous cell carcinoma (HNSCC)

DOSE AND VOLUME DE-ESCALATION OF RADIOTHERAPY IN HEAD AND NECK CANCER

Radiotherapy plays a key role in the treatment of head and neck cancer. However, irradiation of the head and neck region is associated with high rates of acute and chronic toxicity. Technological advances have led to better visualisation of target volumes and critical structures and improved dose conformality in the treatment volume. Despite this, acute toxicity has not been substantially reduced and late toxicity has a significant impact on patients' quality of life. The greater radiosensitivity of tumours associated with the HPV and the development of new imaging techniques have encouraged research into new deintensified strategies to reduce the side effects of radiotherapy. The aim of this paper is to review the literature on the strategies of de-escalated treatment in dose and/or volume in head and neck cancer.

The role of computational methods for automating and improving clinical target volume definition

Treatment planning in radiotherapy distinguishes three target volume concepts: the gross tumor volume (GTV), the clinical target volume (CTV), and the planning target volume (PTV). Over time, GTV definition and PTV margins have improved through the development of novel imaging techniques and better image guidance, respectively. CTV definition is sometimes considered the weakest element in the planning process. CTV definition is particularly complex since the extension of microscopic disease cannot be seen using currently available in-vivo imaging techniques. Instead, CTV definition has to incorporate knowledge of the patterns of tumor progression. While CTV delineation has largely been considered the domain of radiation oncologists, this paper, arising from a 2019 ESTRO Physics research workshop, discusses the contributions that medical physics and computer science can make by developing computational methods to support CTV definition. First, we overview the role of image segmentation algorithms, which may in part automate CTV delineation through segmentation of lymph node stations or normal tissues representing anatomical boundaries of microscopic tumor progression. The recent success of deep convolutional neural networks has also enabled learning entire CTV delineations from examples. Second, we discuss the use of mathematical models of tumor progression for CTV definition, using as example the application of glioma growth models to facilitate GTV-to-CTV expansion for glioblastoma that is consistent with neuroanatomy. We further consider statistical machine learning models to quantify lymphatic metastatic progression of tumors, which may eventually improve elective CTV definition. Lastly, we discuss approaches to incorporate uncertainty in CTV definition into treatment plan optimization as well as general limitations of the CTV concept in the case of infiltrating tumors without natural boundaries.

Automated delineation of the clinical target volume using anatomically constrained 3D expansion of the gross tumor volume

PURPOSE

Delineation of the clinical target volume (CTV) is arguably the weakest link in the treatment planning chain. This work aims to support clinicians in this crucial task.

METHODS AND MATERIALS

While the CTV itself is ambiguous, it is much easier to identify structures that do not belong to the CTV and serve as barriers to the spread of the disease. We segment the known barrier structures using a convolutional neural network (CNN). The CTV is then obtained by starting from the manually delineated gross tumor volume (GTV) and expanding it while taking into account the barrier structures. Mathematically, we define the CTV as an iso-surface in the 3D map of shortest paths of all voxels from the GTV. The shortest paths are found with the Dijkstra algorithm. While the method is generally applicable, we test it on 206 glioma and glioblastoma cases.

RESULTS

The auto-segmented barrier structures for the brain cases include the ventricles, falx cerebri, tentorium cerebelli, brain sinuses, and the outer surface of the brain. Manual and auto-segmented barrier structures agree with surface Dice Similarity Coefficients (DSC) ranging from 0.91 to 0.97 at 2 mm tolerance. Comparison of manual and automatically delineated CTVs shows a median surface DSC of 0.79.

CONCLUSIONS

Barrier structures for CTV definition can be auto-delineated with outstanding precision using a CNN. An algorithm for automated calculation of the CTV by 3D expansion of the GTV while respecting anatomical barriers has been developed. It shows good agreement with manual CTV definition for brain tumors.