Segmentation-based Assessment of Tumor-Vessel Involvement for Surgical
  Resectability Prediction of Pancreatic Ductal Adenocarcinoma
Christiaan Viviers Mark Ramaekers Amaan Valiuddin Terese Hellström Nick Tasios John van der Ven Igor Jacobs Lotte Ewals Joost Nederend Peter de With Misha Luyer Fons van der Sommen
Pancreatic ductal adenocarcinoma (PDAC) is a highly aggressive cancer with
limited treatment options. This research proposes a workflow and deep
learning-based segmentation models to automatically assess tumor-vessel
involvement, a key factor in determining tumor resectability. Correct
assessment of resectability is vital to determine treatment options. The
proposed workflow involves processing CT scans to segment the tumor and
vascular structures, analyzing spatial relationships and the extent of vascular
involvement, which follows a similar way of working as expert radiologists in
PDAC assessment. Three segmentation architectures (nnU-Net, 3D U-Net, and
Probabilistic 3D U-Net) achieve a high accuracy in segmenting veins, arteries,
and the tumor. The segmentations enable automated detection of tumor
involvement with high accuracy (0.88 sensitivity and 0.86 specificity) and
automated computation of the degree of tumor-vessel contact. Additionally, due
to significant inter-observer variability in these important structures, we
present the uncertainty captured by each of the models to further increase
insights into the predicted involvement. This result provides clinicians with a
clear indication of tumor-vessel involvement and may be used to facilitate more
informed decision-making for surgical interventions. The proposed method offers
a valuable tool for improving patient outcomes, personalized treatment
strategies and survival rates in pancreatic cancer.