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Abstract
Histopathological diagnosis of pancreatic ductal adenocarcinoma (PDAC) on endoscopic ultrasonography-guided fine-needle biopsy (EUS-FNB) specimens has become the mainstay of preoperative pathological diagnosis. However, on EUS-FNB specimens, accurate histopathological evaluation is difficult due to low specimen volume with isolated cancer cells and high contamination of blood, inflammatory and digestive tract cells. In this study, we performed annotations for training sets by expert pancreatic pathologists and trained a deep learning model to assess PDAC on EUS-FNB of the pancreas in histopathological whole-slide images. We obtained a high receiver operator curve area under the curve of 0.984, accuracy of 0.9417, sensitivity of 0.9302 and specificity of 0.9706. Our model was able to accurately detect difficult cases of isolated and low volume cancer cells. If adopted as a supportive system in routine diagnosis of pancreatic EUS-FNB specimens, our model has the potential to aid pathologists diagnose difficult cases.
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1 Kurume University School of Medicine, Department of Pathology, Kurume, Japan (GRID:grid.410781.b) (ISNI:0000 0001 0706 0776); Kurume University Hospital, Department of Diagnostic Pathology, Kurume, Japan (GRID:grid.470127.7) (ISNI:0000 0004 1760 3449)
2 Medmain Inc, Medmain Research, Fukuoka, Japan (GRID:grid.470127.7)
3 Jichi Medical University, Department of Pathology, Shimotsuke, Japan (GRID:grid.410804.9) (ISNI:0000000123090000)
4 Kyushu University, Department of Anatomic Pathology, Graduate School of Medical Sciences, Fukuoka, Japan (GRID:grid.177174.3) (ISNI:0000 0001 2242 4849)
5 Kagoshima University, Department of Pathology, Research Field in Medicine and Health Sciences, Medical and Dental Sciences Area, Research and Education Assembly, Sakuragoaka, Japan (GRID:grid.258333.c) (ISNI:0000 0001 1167 1801)
6 Kurashiki Central Hospital, Department of Anatomic Pathology, Kurashiki, Japan (GRID:grid.415565.6) (ISNI:0000 0001 0688 6269)
7 Saga University, Department of Pathology and Microbiology, Saga, Japan (GRID:grid.412339.e) (ISNI:0000 0001 1172 4459); Saga University Hospital, Department of Pathology, Saga, Japan (GRID:grid.416518.f)
8 Shizuoka Cancer Center, Department of Pathology, Shizuoka, Japan (GRID:grid.415797.9) (ISNI:0000 0004 1774 9501)
9 Tokai University Hachioji Hospital, Department of Pathology, Tokyo, Japan (GRID:grid.412762.4) (ISNI:0000 0004 1774 0400)
10 Saitama Medical University, Department of Pathology, Saitama, Japan (GRID:grid.410802.f) (ISNI:0000 0001 2216 2631)
11 Juntendo University, Department of Human Pathology, School of Medicine, Tokyo, Japan (GRID:grid.258269.2) (ISNI:0000 0004 1762 2738)
12 National Cancer Center, Division of Pathology, Exploratory Oncology Research and Clinical Trial Center, Kashiwa, Japan (GRID:grid.272242.3) (ISNI:0000 0001 2168 5385)
13 Tokai University School of Medicine, Department of Pathology, Isehara, Japan (GRID:grid.265061.6) (ISNI:0000 0001 1516 6626)
14 Fukuoka University, Department of Pathology, Faculty of Medicine, Fukuoka, Japan (GRID:grid.411497.e) (ISNI:0000 0001 0672 2176)
15 Saga University Hospital, Department of Pathology, Saga, Japan (GRID:grid.416518.f)
16 Tohoku University Graduate School of Medicine, Department of Investigative Pathology, Sendai, Japan (GRID:grid.69566.3a) (ISNI:0000 0001 2248 6943)
17 Tokyo Medical University, Department of Anatomic Pathology, Tokyo, Japan (GRID:grid.410793.8) (ISNI:0000 0001 0663 3325)
18 Kurume University School of Medicine, Division of Gastroenterology, Department of Medicine, Kurume, Japan (GRID:grid.410781.b) (ISNI:0000 0001 0706 0776)
19 Kurume University Hospital, Department of Diagnostic Pathology, Kurume, Japan (GRID:grid.470127.7) (ISNI:0000 0004 1760 3449)
20 Kurume University School of Medicine, Department of Pathology, Kurume, Japan (GRID:grid.410781.b) (ISNI:0000 0001 0706 0776)