Improved Pancreatic Tumor Detection by Utilizing Clinically-Relevant Secondary Features

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Abstract

Pancreatic cancer is one of the global leading causes of cancer-
related deaths. Despite the success of Deep Learning in computer-aided
diagnosis and detection (CAD) methods, little attention has been paid
to the detection of Pancreatic Cancer. We propose a method for de-
tecting pancreatic tumor that utilizes clinically-relevant features in the
surrounding anatomical structures, thereby better aiming to exploit the
radiologist’s knowledge compared to other, conventional deep learning
approaches. To this end, we collect a new dataset consisting of 99 cases
with pancreatic ductal adenocarcinoma (PDAC) and 97 control cases
without any pancreatic tumor. Due to the growth pattern of pancre-
atic cancer, the tumor may not be always visible as a hypodense lesion,
therefore experts refer to the visibility of secondary external features that
may indicate the presence of the tumor. We propose a method based on
a U-Net-like Deep CNN that exploits the following external secondary
features: the pancreatic duct, common bile duct and the pancreas, along
with a processed CT scan. Using these features, the model segments
the pancreatic tumor if it is present. This segmentation for classification
and localization approach achieves a performance of 99% sensitivity (one
case missed) and 99% specificity, which realizes a 5% increase in sensi-
tivity over the previous state-of-the-art method. The model additionally
provides location information with reasonable accuracy and a shorter
inference time compared to previous PDAC detection methods. These
results offer a significant performance improvement and highlight the
importance of incorporating the knowledge of the clinical expert when
developing novel CAD methods.
Original languageEnglish
Title of host publicationCancer Prevention Through Early Detection - 1st International Workshop, CaPTion 2022, Held in Conjunction with MICCAI 2022, Proceedings
Subtitle of host publicationFirst International Workshop, CaPTion 2022, Held in Conjunction with MICCAI 2022, Singapore, September 22, 2022, Proceedings
EditorsSharib Ali, Fons van der Sommen, Maureen van Eijnatten, Iris Kolenbrander, Bartłomiej Władysław Papież, Yueming Jin
PublisherSpringer
Chapter14
Pages139-148
Number of pages10
ISBN (Print)978-3-031-17978-5
DOIs
Publication statusPublished - 2022
Event25th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2022 - Singapore, Singapore
Duration: 18 Sept 202222 Sept 2022
Conference number: 25

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume13581 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference25th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2022
Abbreviated titleMICCAI 2022
Country/TerritorySingapore
CitySingapore
Period18/09/2222/09/22

Keywords

  • Pancreatic Cancer
  • Tumor Segmentation
  • CNN
  • Pancreatic cancer
  • Tumor segmentation

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