Annotation-Efficient Deep Learning Model for Pancreatic Cancer Diagnosis and Classification Using CT Images: A Retrospective Diagnostic Study

Author:

Viriyasaranon Thanaporn1,Chun Jung Won2ORCID,Koh Young Hwan2,Cho Jae Hee3ORCID,Jung Min Kyu4ORCID,Kim Seong-Hun5ORCID,Kim Hyo Jung6,Lee Woo Jin2ORCID,Choi Jang-Hwan1ORCID,Woo Sang Myung2ORCID

Affiliation:

1. Graduate Program in System Health Science and Engineering, Division of Mechanical and Biomedical Engineering, Ewha Womans University, Seoul 03760, Republic of Korea

2. Center for Liver and Pancreatobiliary Cancer, National Cancer Center, Goyang 10408, Republic of Korea

3. Department of Internal Medicine, Gangnam Severance Hospital, Yonsei University College of Medicine, Seoul 03722, Republic of Korea

4. Department of Internal Medicine, Kyungpook National University Hospital, Daegu 41944, Republic of Korea

5. Department of Internal Medicine, Research Institute of Clinical Medicine of Jeonbuk National University—Biomedical Research Institute of Jeonbuk National University Hospital, Jeonju 54907, Republic of Korea

6. Department of Gastroenterology, Korea University Guro Hospital, Seoul 10408, Republic of Korea

Abstract

The aim of this study was to develop a novel deep learning (DL) model without requiring large-annotated training datasets for detecting pancreatic cancer (PC) using computed tomography (CT) images. This retrospective diagnostic study was conducted using CT images collected from 2004 and 2019 from 4287 patients diagnosed with PC. We proposed a self-supervised learning algorithm (pseudo-lesion segmentation (PS)) for PC classification, which was trained with and without PS and validated on randomly divided training and validation sets. We further performed cross-racial external validation using open-access CT images from 361 patients. For internal validation, the accuracy and sensitivity for PC classification were 94.3% (92.8–95.4%) and 92.5% (90.0–94.4%), and 95.7% (94.5–96.7%) and 99.3 (98.4–99.7%) for the convolutional neural network (CNN) and transformer-based DL models (both with PS), respectively. Implementing PS on a small-sized training dataset (randomly sampled 10%) increased accuracy by 20.5% and sensitivity by 37.0%. For external validation, the accuracy and sensitivity were 82.5% (78.3–86.1%) and 81.7% (77.3–85.4%) and 87.8% (84.0–90.8%) and 86.5% (82.3–89.8%) for the CNN and transformer-based DL models (both with PS), respectively. PS self-supervised learning can increase DL-based PC classification performance, reliability, and robustness of the model for unseen, and even small, datasets. The proposed DL model is potentially useful for PC diagnosis.

Funder

National Cancer Center

National Research Foundation of Korea

Korean government

Publisher

MDPI AG

Subject

Cancer Research,Oncology

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