  Head and neck cancers are the fifth most common cancer worldwide, and
recently, analysis of Positron Emission Tomography (PET) and Computed
Tomography (CT) images has been proposed to identify patients with a prognosis.
Even though the results look promising, more research is needed to further
validate and improve the results. This paper presents the work done by team MLC
for the 2022 version of the HECKTOR grand challenge held at MICCAI 2022. For
Task 1, the automatic segmentation task, our approach was, in contrast to
earlier solutions using 3D segmentation, to keep it as simple as possible using
a 2D model, analyzing every slice as a standalone image. In addition, we were
interested in understanding how different modalities influence the results. We
proposed two approaches; one using only the CT scans to make predictions and
another using a combination of the CT and PET scans. For Task 2, the prediction
of recurrence-free survival, we first proposed two approaches, one where we
only use patient data and one where we combined the patient data with
segmentations from the image model. For the prediction of the first two
approaches, we used Random Forest. In our third approach, we combined patient
data and image data using XGBoost. Low kidney function might worsen cancer
prognosis. In this approach, we therefore estimated the kidney function of the
patients and included it as a feature. Overall, we conclude that our simple
methods were not able to compete with the highest-ranking submissions, but we
still obtained reasonably good scores. We also got interesting insights into
how the combination of different modalities can influence the segmentation and
predictions.
