Deep learning for multiclass tumor cell detection in histopathology slides of hereditary diffuse gastric cancer
Summary: Hereditary diffuse gastric cancer (HDGC) is a rare condition where early tumor detection is challenging due to diffuse infiltration and tumor heterogeneity. Accurate identification of DGC cells is essential for understanding tumor behavior. This study aimed to develop deep learning models t...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-08-01
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| Series: | iScience |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2589004225013252 |
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| author | Robin Lomans Valentina Angerilli Joey Spronck Liudmila L. Kodach Irene Gullo Fátima Carneiro Rachel S. van der Post Francesco Ciompi |
| author_facet | Robin Lomans Valentina Angerilli Joey Spronck Liudmila L. Kodach Irene Gullo Fátima Carneiro Rachel S. van der Post Francesco Ciompi |
| author_sort | Robin Lomans |
| collection | DOAJ |
| description | Summary: Hereditary diffuse gastric cancer (HDGC) is a rare condition where early tumor detection is challenging due to diffuse infiltration and tumor heterogeneity. Accurate identification of DGC cells is essential for understanding tumor behavior. This study aimed to develop deep learning models to automatically detect key tumor cell types—typical and atypical signet ring cells and non-signet ring tumor cells—in H&E-stained digital pathology slides from HDGC patients. Using a multi-center dataset of 350 whole-slide images and over 91,000 annotated cells from 43 patients, we trained nnU-Net models for cell detection and compared them to Faster R-CNN baselines. We also conducted a reader study with five pathologists to benchmark performance. nnU-Net outperformed both pathologist inter-observer agreement and Faster R-CNN, achieving an F1 score of 0.49. It also matched human-level performance in estimating lesion size and cell type distributions, demonstrating its potential to support DGC tumor detection and analysis. |
| format | Article |
| id | doaj-art-de4a42d4edcb4089924057b167d1d561 |
| institution | DOAJ |
| issn | 2589-0042 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | Elsevier |
| record_format | Article |
| series | iScience |
| spelling | doaj-art-de4a42d4edcb4089924057b167d1d5612025-08-20T03:12:56ZengElsevieriScience2589-00422025-08-0128811306410.1016/j.isci.2025.113064Deep learning for multiclass tumor cell detection in histopathology slides of hereditary diffuse gastric cancerRobin Lomans0Valentina Angerilli1Joey Spronck2Liudmila L. Kodach3Irene Gullo4Fátima Carneiro5Rachel S. van der Post6Francesco Ciompi7Department of Pathology, Radboud University Medical Centre, Nijmegen, the Netherlands; Corresponding authorDepartment of Pathology, Radboud University Medical Centre, Nijmegen, the NetherlandsDepartment of Pathology, Radboud University Medical Centre, Nijmegen, the NetherlandsDepartment of Pathology, The Netherlands Cancer Institute, Amsterdam, the NetherlandsDepartment of Pathology, Unidade Local de Saúde São João, Porto, Portugal; Department of Pathology, Faculty of Medicine of the University of Porto (FMUP), Porto, Portugal; Instituto de Investigação E Inovação Em Saúde (i3S) & Institute of Molecular Pathology and Immunology of the University of Porto (Ipatimup), Porto, PortugalDepartment of Pathology, Unidade Local de Saúde São João, Porto, Portugal; Department of Pathology, Faculty of Medicine of the University of Porto (FMUP), Porto, Portugal; Instituto de Investigação E Inovação Em Saúde (i3S) & Institute of Molecular Pathology and Immunology of the University of Porto (Ipatimup), Porto, PortugalDepartment of Pathology, Radboud University Medical Centre, Nijmegen, the Netherlands; Corresponding authorDepartment of Pathology, Radboud University Medical Centre, Nijmegen, the Netherlands; Corresponding authorSummary: Hereditary diffuse gastric cancer (HDGC) is a rare condition where early tumor detection is challenging due to diffuse infiltration and tumor heterogeneity. Accurate identification of DGC cells is essential for understanding tumor behavior. This study aimed to develop deep learning models to automatically detect key tumor cell types—typical and atypical signet ring cells and non-signet ring tumor cells—in H&E-stained digital pathology slides from HDGC patients. Using a multi-center dataset of 350 whole-slide images and over 91,000 annotated cells from 43 patients, we trained nnU-Net models for cell detection and compared them to Faster R-CNN baselines. We also conducted a reader study with five pathologists to benchmark performance. nnU-Net outperformed both pathologist inter-observer agreement and Faster R-CNN, achieving an F1 score of 0.49. It also matched human-level performance in estimating lesion size and cell type distributions, demonstrating its potential to support DGC tumor detection and analysis.http://www.sciencedirect.com/science/article/pii/S2589004225013252CancerArtificial intelligence |
| spellingShingle | Robin Lomans Valentina Angerilli Joey Spronck Liudmila L. Kodach Irene Gullo Fátima Carneiro Rachel S. van der Post Francesco Ciompi Deep learning for multiclass tumor cell detection in histopathology slides of hereditary diffuse gastric cancer iScience Cancer Artificial intelligence |
| title | Deep learning for multiclass tumor cell detection in histopathology slides of hereditary diffuse gastric cancer |
| title_full | Deep learning for multiclass tumor cell detection in histopathology slides of hereditary diffuse gastric cancer |
| title_fullStr | Deep learning for multiclass tumor cell detection in histopathology slides of hereditary diffuse gastric cancer |
| title_full_unstemmed | Deep learning for multiclass tumor cell detection in histopathology slides of hereditary diffuse gastric cancer |
| title_short | Deep learning for multiclass tumor cell detection in histopathology slides of hereditary diffuse gastric cancer |
| title_sort | deep learning for multiclass tumor cell detection in histopathology slides of hereditary diffuse gastric cancer |
| topic | Cancer Artificial intelligence |
| url | http://www.sciencedirect.com/science/article/pii/S2589004225013252 |
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