Artificial Intelligence in Head-and-Neck Cancer Radiation Oncology
Authors:   , Strijbis Victor Immanuel Johannes 
Published: November 2024

Radiotherapy is a cornerstone treatment for head-and-neck cancer and involves (1) contouring target areas and healthy tissues, and (2) treatment planning. Convolutional neural networks can help automate these processes because of their ability to map complex (non-linear) relationships in large amounts of imaging data. This dissertation addresses how CNNs can be used to improve the precision and efficiency of radiotherapy, with imaging as the basis.

In chapter 2, we validated convolutional neural networks for simultaneous segmentation of healthy eye tissue, tumor volume, and retinal detachment in high-resolution MRI in retinoblastoma patients. In this multi-center study, we investigated branched (multi-view; 2.5D) MVCNNs as an alternative to the established UNet to resolve local ambiguities where classes are difficult to separate and training data are scarce. In 14 independent patients, MVCNN significantly (p<1.0x10-3) outperformed UNet, with DSCs (agreement) of 78%, 64%, and 87% for tumor, retinal detachment, and eye sclera, respectively, compared to 66%, 50%, and 67% by UNet.

In locally advanced head-and-neck cancer, elective lymph node levels are irradiated to reduce the risk of metastasis. In chapter 3, we addressed automatic segmentation of the elective planning target volume. We compared three network configurations to segment individual lymph node levels (I-V), using (1) UNet; (2) MVCNN; and (3) UNet+MVCNN. In 10 independent patients, UNet adequately identified lymph node levels I-V with mean DSC=77.6%, and the addition of MVCNN refined this result with a mean DSC=79.2%.

Errors and inconsistencies inherently occur in reference contours of radiotherapy data. However, the impact of these imperfections on the CNNs is not well understood. In chapter 4, we used the parotid gland to investigate this impact and explore a simple method for automatic curation. When removing poorly performing cases from the training set the model’s generalizability consistently improved without affecting performance on independent cases from our own hospital. Quantitatively, auto-curation significantly (p<1.0x10-12) improved DSC on external cases from 86.1% to 87.3%.

The “black-box”-nature of CNNs complicates understanding when a CNN might fail, hindering clinical implementation. In chapter 5, we explored whether explainable artificial intelligence may potentially be used to flag inadequate performance. We investigated six commonly used explainable artificial intelligence methods in the context of segmentation in simulated and clinical cases. To objectify results, we used persistent homology to extract relevant explainable features, and correlated them to a-priori estimate the probability of failure for a given case. Guided backpropagation and PatternNet (attributions) qualitatively agreed most with expert reasoning, while persistent homology of guided gradient class-activation mapping could mark inadequate contours with high (>85%) sensitivity and excellent (>90%) specificity.

After contouring, a treatment plan is configured, which is a time-consuming and complex process. In chapter 6, we investigated how conditional generative adversarial networks can predict possible dose distributions for a given case. Additionally, we explored how different input information affected accuracy of predicted dose distributions. We distinguished between (1) anatomical information; (2) healthy organs; (3) target structures and (4) body contour. Networks did not require anatomical information for accurate dose prediction other than the target contour.