Abstract
Several recent PET/CT radiomics studies have shown promising results for the prediction of patient outcomes in Head and Neck (H&N) cancer. These studies, however, are most often conducted on relatively small cohorts (up to 300 patients) and using manually delineated tumors. Recently, deep learning reached high performance in the automatic segmentation of H&N primary tumors in PET/CT. The automatic segmentation could be used to validate these studies on larger-scale cohorts while obviating the burden of manual delineation. We propose a complete PET/CT processing pipeline gathering the automatic segmentation of primary tumors and prognosis prediction of patients with H&N cancer treated with radiotherapy and chemotherapy. Automatic contours of the primary Gross Tumor Volume (GTVt) are obtained from a 3D UNet. A radiomics pipeline that automatically predicts the patient outcome (Disease Free Survival, DFS) is compared when using either the automatically or the manually annotated contours. In addition, we extract deep features from the bottleneck layers of the 3D UNet to compare them with standard radiomics features (first- and second-order as well as shape features) and to test the performance gain when added to them. The models are evaluated on the HECKTOR 2020 dataset consisting of 239 H&N patients with PET, CT, GTVt contours and DFS data available (five centers). Regarding the results, using Hand-Crafted (HC) radiomics features extracted from manual GTVt achieved the best performance and is associated with an average Concordance (C) index of 0.672. The fully automatic pipeline (including deep and HC features from automatic GTVt) achieved an average C index of 0.626, which is lower but relatively close to using manual GTVt (p-value = 0.20). This suggests that large-scale studies could be conducted using a fully automatic pipeline to further validate the current state of the art H&N radiomics. The code will be shared publicly for reproducibility.
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Notes
- 1.
We can unconventionally detail the number of HC features as follows: \(274 = 2 \text { modalities}\times (2\text { bin}\times 56 \text { 2}^{nd}\text {order} + 18 \text { 1}^{st}\text {order}) + 14 \text { shape}\) (see Table 2).
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Acknowledgments
This work was partially supported by the Swiss National Science Foundation (SNSF, grant 205320_179069), the Swiss Personalized Health Network (SPHN via the IMAGINE and QA4IQI projects), and the Hasler Foundation (via the EPICS project, grant 20004).
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Fontaine, P. et al. (2021). Fully Automatic Head and Neck Cancer Prognosis Prediction in PET/CT. In: Syeda-Mahmood, T., et al. Multimodal Learning for Clinical Decision Support. ML-CDS 2021. Lecture Notes in Computer Science(), vol 13050. Springer, Cham. https://doi.org/10.1007/978-3-030-89847-2_6
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