Prediction of post-radiotherapy recurrence volumes in head and neck
  squamous cell carcinoma using 3D U-Net segmentation
Denis Kutnár Ivan R Vogelius Katrin Elisabet Håkansson Jens Petersen Jeppe Friborg Lena Specht Mogens Bernsdorf Anita Gothelf Claus Kristensen Abraham George Smith
Locoregional recurrences (LRR) are still a frequent site of treatment failure
for head and neck squamous cell carcinoma (HNSCC) patients.
  Identification of high risk subvolumes based on pretreatment imaging is key
to biologically targeted radiation therapy. We investigated the extent to which
a Convolutional neural network (CNN) is able to predict LRR volumes based on
pre-treatment 18F-fluorodeoxyglucose positron emission tomography
(FDG-PET)/computed tomography (CT) scans in HNSCC patients and thus the
potential to identify biological high risk volumes using CNNs.
  For 37 patients who had undergone primary radiotherapy for oropharyngeal
squamous cell carcinoma, five oncologists contoured the relapse volumes on
recurrence CT scans. Datasets of pre-treatment FDG-PET/CT, gross tumour volume
(GTV) and contoured relapse for each of the patients were randomly divided into
training (n=23), validation (n=7) and test (n=7) datasets. We compared a CNN
trained from scratch, a pre-trained CNN, a SUVmax threshold approach, and using
the GTV directly.
  The SUVmax threshold method included 5 out of the 7 relapse origin points
within a volume of median 4.6 cubic centimetres (cc). Both the GTV contour and
best CNN segmentations included the relapse origin 6 out of 7 times with median
volumes of 28 and 18 cc respectively.
  The CNN included the same or greater number of relapse volume POs, with
significantly smaller relapse volumes. Our novel findings indicate that CNNs
may predict LRR, yet further work on dataset development is required to attain
clinically useful prediction accuracy.