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

MLC at HECKTOR 2022: The Effect and Importance of Training Data when Analyzing Cases of Head and Neck Tumors using Machine Learning