Liquid-based cytological diagnosis of pancreatic neuroendocrine tumors using hyperspectral imaging and deep learning
The incidence of pancreatic neuroendocrine tumors (PanNETs), although uncommon, has recently increased, and they are almost always diagnosed in the late stages due to their indolent clinical manifestations. This study developed a method that combines hyperspectral imaging (HSI) technology and a conv...
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Elsevier
2025-03-01
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| Series: | EngMedicine |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2950489925000053 |
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| author | Taojing Ran Wei Huang Xianzheng Qin Xingran Xie Yingjiao Deng Yundi Pan Yao Zhang Ling Zhang Lili Gao Minmin Zhang Dong Wang Yan Wang Qingli Li Chunhua Zhou Duowu Zou |
| author_facet | Taojing Ran Wei Huang Xianzheng Qin Xingran Xie Yingjiao Deng Yundi Pan Yao Zhang Ling Zhang Lili Gao Minmin Zhang Dong Wang Yan Wang Qingli Li Chunhua Zhou Duowu Zou |
| author_sort | Taojing Ran |
| collection | DOAJ |
| description | The incidence of pancreatic neuroendocrine tumors (PanNETs), although uncommon, has recently increased, and they are almost always diagnosed in the late stages due to their indolent clinical manifestations. This study developed a method that combines hyperspectral imaging (HSI) technology and a convolutional neural network (CNN) to conduct a cytological diagnosis of PanNETs. We acquired hyperspectral information from the nuclei of PanNETs and benign cells derived from liquid-based cytology (LBC) of pancreatic endoscopic ultrasound-guided fine-needle aspiration/biopsy specimens. CNN model was trained to distinguish among different cell types based on spectral and spatial differences compared with conventional “red, green, and blue (RGB)” images. For cell classification, the CNN system identified hyperspectral images containing PanNETs and benign cells with areas under the curve (AUCs) of 0.9981 and 0.9815 in datasets from two different time points, respectively, showing superior performance compared to the conventional RGB group, with corresponding AUCs of 0.9716 and 0.9550. Higher accuracy was achieved for the HSI group than for the RGB group in both test datasets (94.92 % versus 89.85 % and 93.19 % versus 80.63 %, respectively). Our results revealed that HSI-based cytological diagnosis using a CNN could provide superior classification performance for PanNETs compared with conventional RGB images. With further validation, this innovative technique can be utilized as an alternative to traditional cytological diagnosis for higher efficiency, thus reducing the workload of daily clinical practice. |
| format | Article |
| id | doaj-art-baaccf8b499246f488c170c75a13abff |
| institution | Kabale University |
| issn | 2950-4899 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Elsevier |
| record_format | Article |
| series | EngMedicine |
| spelling | doaj-art-baaccf8b499246f488c170c75a13abff2025-01-31T05:12:54ZengElsevierEngMedicine2950-48992025-03-0121100059Liquid-based cytological diagnosis of pancreatic neuroendocrine tumors using hyperspectral imaging and deep learningTaojing Ran0Wei Huang1Xianzheng Qin2Xingran Xie3Yingjiao Deng4Yundi Pan5Yao Zhang6Ling Zhang7Lili Gao8Minmin Zhang9Dong Wang10Yan Wang11Qingli Li12Chunhua Zhou13Duowu Zou14Department of Gastroenterology, Ruijin Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai 200025, ChinaDepartment of Gastroenterology, Ruijin Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai 200025, ChinaDepartment of Gastroenterology, Ruijin Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai 200025, ChinaShanghai Key Laboratory of Multidimensional Information Processing, East China Normal University, Shanghai 200241, ChinaShanghai Key Laboratory of Multidimensional Information Processing, East China Normal University, Shanghai 200241, ChinaDepartment of Gastroenterology, Ruijin Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai 200025, ChinaDepartment of Gastroenterology, Ruijin Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai 200025, ChinaDepartment of Gastroenterology, Ruijin Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai 200025, ChinaDepartment of Pathology, Ruijin Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai 200025, ChinaDepartment of Gastroenterology, Ruijin Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai 200025, ChinaDepartment of Gastroenterology, Ruijin Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai 200025, ChinaShanghai Key Laboratory of Multidimensional Information Processing, East China Normal University, Shanghai 200241, ChinaShanghai Key Laboratory of Multidimensional Information Processing, East China Normal University, Shanghai 200241, ChinaDepartment of Gastroenterology, Ruijin Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China; Corresponding author. Department of Gastroenterology, Ruijin Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, 197 Ruijin Second Road, Shanghai 200025, China.Department of Gastroenterology, Ruijin Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China; Corresponding author. Department of Gastroenterology, Ruijin Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, 197 Ruijin Second Road, Shanghai 200025, China.The incidence of pancreatic neuroendocrine tumors (PanNETs), although uncommon, has recently increased, and they are almost always diagnosed in the late stages due to their indolent clinical manifestations. This study developed a method that combines hyperspectral imaging (HSI) technology and a convolutional neural network (CNN) to conduct a cytological diagnosis of PanNETs. We acquired hyperspectral information from the nuclei of PanNETs and benign cells derived from liquid-based cytology (LBC) of pancreatic endoscopic ultrasound-guided fine-needle aspiration/biopsy specimens. CNN model was trained to distinguish among different cell types based on spectral and spatial differences compared with conventional “red, green, and blue (RGB)” images. For cell classification, the CNN system identified hyperspectral images containing PanNETs and benign cells with areas under the curve (AUCs) of 0.9981 and 0.9815 in datasets from two different time points, respectively, showing superior performance compared to the conventional RGB group, with corresponding AUCs of 0.9716 and 0.9550. Higher accuracy was achieved for the HSI group than for the RGB group in both test datasets (94.92 % versus 89.85 % and 93.19 % versus 80.63 %, respectively). Our results revealed that HSI-based cytological diagnosis using a CNN could provide superior classification performance for PanNETs compared with conventional RGB images. With further validation, this innovative technique can be utilized as an alternative to traditional cytological diagnosis for higher efficiency, thus reducing the workload of daily clinical practice.http://www.sciencedirect.com/science/article/pii/S2950489925000053Pancreatic neuroendocrine tumorsArtificial intelligenceHyperspectral imagingLiquid-based cytology |
| spellingShingle | Taojing Ran Wei Huang Xianzheng Qin Xingran Xie Yingjiao Deng Yundi Pan Yao Zhang Ling Zhang Lili Gao Minmin Zhang Dong Wang Yan Wang Qingli Li Chunhua Zhou Duowu Zou Liquid-based cytological diagnosis of pancreatic neuroendocrine tumors using hyperspectral imaging and deep learning EngMedicine Pancreatic neuroendocrine tumors Artificial intelligence Hyperspectral imaging Liquid-based cytology |
| title | Liquid-based cytological diagnosis of pancreatic neuroendocrine tumors using hyperspectral imaging and deep learning |
| title_full | Liquid-based cytological diagnosis of pancreatic neuroendocrine tumors using hyperspectral imaging and deep learning |
| title_fullStr | Liquid-based cytological diagnosis of pancreatic neuroendocrine tumors using hyperspectral imaging and deep learning |
| title_full_unstemmed | Liquid-based cytological diagnosis of pancreatic neuroendocrine tumors using hyperspectral imaging and deep learning |
| title_short | Liquid-based cytological diagnosis of pancreatic neuroendocrine tumors using hyperspectral imaging and deep learning |
| title_sort | liquid based cytological diagnosis of pancreatic neuroendocrine tumors using hyperspectral imaging and deep learning |
| topic | Pancreatic neuroendocrine tumors Artificial intelligence Hyperspectral imaging Liquid-based cytology |
| url | http://www.sciencedirect.com/science/article/pii/S2950489925000053 |
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