Deep learning model for detecting high-grade dysplasia in colorectal adenomas
Objective: Early detection and removal of suspicious polyps during routine colonoscopies play an important role in reducing the risk of colorectal cancer. Patient management and follow-up are determined by the type of polyps removed and the degree of dysplasia present on histological evaluation. Whe...
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Elsevier
2025-04-01
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| Series: | Journal of Pathology Informatics |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2153353925000264 |
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| author | Eric Steimetz Zeliha Celen Simsek Asmita Saha Rong Xia Raavi Gupta |
| author_facet | Eric Steimetz Zeliha Celen Simsek Asmita Saha Rong Xia Raavi Gupta |
| author_sort | Eric Steimetz |
| collection | DOAJ |
| description | Objective: Early detection and removal of suspicious polyps during routine colonoscopies play an important role in reducing the risk of colorectal cancer. Patient management and follow-up are determined by the type of polyps removed and the degree of dysplasia present on histological evaluation. Whereas discerning between a benign polyp and a dysplastic one is a trivial task, distinguishing between tubular adenomas with low-grade dysplasia (LGD) and high-grade dysplasia (HGD) is a challenging task. In this study, we trained a deep learning model to distinguish between colorectal adenomas with LGD and HGD. Design: We retrieved 259 slides of adenomatous polyps taken between January 2011 and October 2024. Slides with HGD were reviewed by a subspecialty-trained GI pathologist. After excluding discordant and duplicate cases, 200 slides remained: 71 (35.5%) with HGD and 129 (64.5%) with LGD. The slides were divided into training (160 slides, 80%) and test (40 slides, 20%) sets. After patch generation and stain normalization, a ResNet34 model (pre-trained on ImageNet) was trained using 5-fold cross-validation. Slide classification was determined by aggregating patch-level predictions. Results: The model's slide-level prediction accuracy was 95.0%, correctly classifying all but 2 out of 40 slides. The model achieved an area under the receiver operating characteristic curve score of 0.981 and an F1 score of 0.923. Conclusions: This study demonstrates that deep learning models can accurately distinguish between colonic adenomas with LGD and HGD. Training on a larger dataset could increase the accuracy and generalizability of the model and should be a focus of further studies. |
| format | Article |
| id | doaj-art-58e2158230d0439084b2d41b8e596274 |
| institution | Kabale University |
| issn | 2153-3539 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Journal of Pathology Informatics |
| spelling | doaj-art-58e2158230d0439084b2d41b8e5962742025-08-20T03:53:51ZengElsevierJournal of Pathology Informatics2153-35392025-04-011710044110.1016/j.jpi.2025.100441Deep learning model for detecting high-grade dysplasia in colorectal adenomasEric Steimetz0Zeliha Celen Simsek1Asmita Saha2Rong Xia3Raavi Gupta4Department of Pathology, SUNY Downstate Health Sciences University, Brooklyn, NY, USA; Corresponding author at: 450 Clarkson Avenue, MSC 25, Brooklyn, NY 11203, USA.Department of Pathology, SUNY Downstate Health Sciences University, Brooklyn, NY, USADepartment of Pathology, SUNY Downstate Health Sciences University, Brooklyn, NY, USADepartment of Pathology, New York University School of Medicine, New York, NY, USADepartment of Pathology, SUNY Downstate Health Sciences University, Brooklyn, NY, USAObjective: Early detection and removal of suspicious polyps during routine colonoscopies play an important role in reducing the risk of colorectal cancer. Patient management and follow-up are determined by the type of polyps removed and the degree of dysplasia present on histological evaluation. Whereas discerning between a benign polyp and a dysplastic one is a trivial task, distinguishing between tubular adenomas with low-grade dysplasia (LGD) and high-grade dysplasia (HGD) is a challenging task. In this study, we trained a deep learning model to distinguish between colorectal adenomas with LGD and HGD. Design: We retrieved 259 slides of adenomatous polyps taken between January 2011 and October 2024. Slides with HGD were reviewed by a subspecialty-trained GI pathologist. After excluding discordant and duplicate cases, 200 slides remained: 71 (35.5%) with HGD and 129 (64.5%) with LGD. The slides were divided into training (160 slides, 80%) and test (40 slides, 20%) sets. After patch generation and stain normalization, a ResNet34 model (pre-trained on ImageNet) was trained using 5-fold cross-validation. Slide classification was determined by aggregating patch-level predictions. Results: The model's slide-level prediction accuracy was 95.0%, correctly classifying all but 2 out of 40 slides. The model achieved an area under the receiver operating characteristic curve score of 0.981 and an F1 score of 0.923. Conclusions: This study demonstrates that deep learning models can accurately distinguish between colonic adenomas with LGD and HGD. Training on a larger dataset could increase the accuracy and generalizability of the model and should be a focus of further studies.http://www.sciencedirect.com/science/article/pii/S2153353925000264GI polypsTubular adenomaHigh-grade dysplasiaDeep learningWhole-slide images |
| spellingShingle | Eric Steimetz Zeliha Celen Simsek Asmita Saha Rong Xia Raavi Gupta Deep learning model for detecting high-grade dysplasia in colorectal adenomas Journal of Pathology Informatics GI polyps Tubular adenoma High-grade dysplasia Deep learning Whole-slide images |
| title | Deep learning model for detecting high-grade dysplasia in colorectal adenomas |
| title_full | Deep learning model for detecting high-grade dysplasia in colorectal adenomas |
| title_fullStr | Deep learning model for detecting high-grade dysplasia in colorectal adenomas |
| title_full_unstemmed | Deep learning model for detecting high-grade dysplasia in colorectal adenomas |
| title_short | Deep learning model for detecting high-grade dysplasia in colorectal adenomas |
| title_sort | deep learning model for detecting high grade dysplasia in colorectal adenomas |
| topic | GI polyps Tubular adenoma High-grade dysplasia Deep learning Whole-slide images |
| url | http://www.sciencedirect.com/science/article/pii/S2153353925000264 |
| work_keys_str_mv | AT ericsteimetz deeplearningmodelfordetectinghighgradedysplasiaincolorectaladenomas AT zelihacelensimsek deeplearningmodelfordetectinghighgradedysplasiaincolorectaladenomas AT asmitasaha deeplearningmodelfordetectinghighgradedysplasiaincolorectaladenomas AT rongxia deeplearningmodelfordetectinghighgradedysplasiaincolorectaladenomas AT raavigupta deeplearningmodelfordetectinghighgradedysplasiaincolorectaladenomas |