Application of deep learning convolutional neural networks to identify gastric squamous cell carcinoma in mice
ObjectiveIn non-clinical safety evaluation of drugs, pathological result is one of the gold standards for determining toxic effects. However, pathological diagnosis might be challenging and affected by pathologist expertise. In carcinogenicity studies, drug-induced squamous cell carcinoma (SCC) of t...
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Frontiers Media S.A.
2025-05-01
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| Series: | Frontiers in Medicine |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fmed.2025.1587417/full |
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| author | Yuke Ren Yuke Ren Shuangxing Li Di Zhang Yongtian Zhao Yanwei Yang Guitao Huo Xiaobing Zhou Xingchao Geng Zhi Lin Zhe Qu |
| author_facet | Yuke Ren Yuke Ren Shuangxing Li Di Zhang Yongtian Zhao Yanwei Yang Guitao Huo Xiaobing Zhou Xingchao Geng Zhi Lin Zhe Qu |
| author_sort | Yuke Ren |
| collection | DOAJ |
| description | ObjectiveIn non-clinical safety evaluation of drugs, pathological result is one of the gold standards for determining toxic effects. However, pathological diagnosis might be challenging and affected by pathologist expertise. In carcinogenicity studies, drug-induced squamous cell carcinoma (SCC) of the mouse stomach represents a diagnostic challenge for toxicopathologists. This study aims to establish a detection model for mouse gastric squamous cell carcinoma (GSCC) using deep learning algorithms, to improve the accuracy and consistency of pathological diagnoses.MethodsA total of 93 cases of drug-induced mouse GSCC and 56 cases of normal mouse stomach tissue from carcinogenicity studies were collected. After scanning into digital slides, semi-automated data annotation was performed. All images underwent preprocessing, including tissue extraction, artifact removal, and exclusion of normal epithelial regions. The images were then randomly divided into training, validation, and test sets in an 8:1:1 ratio. Five different convolutional neural networks (CNNs)-FCN, LR-ASPP, DeepLabv3+, U-Net, and DenseNet were applied to identify GSCC and non-GSCC regions. Tumor prediction images (algorithm results shown as overlays) derived from the slide images were compared, and the performance of the constructed models was evaluated using Precision, Recall, and F1-score.ResultsThe Precision, Recall, and F1-scores of DenseNet, U-Net, and DeepLabv3 + algorithms were all above 90%. Specifically, the DenseNet model achieved an overall Precision of 0.9044, Recall of 0.9291, and F1-score of 0.9157 in the test set. Compared to the other algorithms, DenseNet exhibited the highest F1-score and Recall, demonstrating superior generalization ability.ConclusionThe DenseNet algorithm model developed in this study shown promising application potential for assisting in the diagnosis of mouse GSCC. As artificial intelligence (AI) technology continues to advance in non-clinical safety evaluation of drugs, CNN-based toxicological pathology detection models will become essential tools to assist pathologists in precise diagnosis and consistency evaluation. |
| format | Article |
| id | doaj-art-168456a6bb5d4f0fb19b60ead070cfb2 |
| institution | Kabale University |
| issn | 2296-858X |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Medicine |
| spelling | doaj-art-168456a6bb5d4f0fb19b60ead070cfb22025-08-20T03:52:47ZengFrontiers Media S.A.Frontiers in Medicine2296-858X2025-05-011210.3389/fmed.2025.15874171587417Application of deep learning convolutional neural networks to identify gastric squamous cell carcinoma in miceYuke Ren0Yuke Ren1Shuangxing Li2Di Zhang3Yongtian Zhao4Yanwei Yang5Guitao Huo6Xiaobing Zhou7Xingchao Geng8Zhi Lin9Zhe Qu10National Institutes for Food and Drug Control, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, ChinaNational Center for Safety Evaluation of Drugs, State Key Laboratory of Drug Regulatory Science, National Institutes for Food and Drug Control, Beijing, ChinaNational Center for Safety Evaluation of Drugs, State Key Laboratory of Drug Regulatory Science, National Institutes for Food and Drug Control, Beijing, ChinaNational Center for Safety Evaluation of Drugs, State Key Laboratory of Drug Regulatory Science, National Institutes for Food and Drug Control, Beijing, ChinaIndica Labs, Inc., Albuquerque, NM, United StatesNational Center for Safety Evaluation of Drugs, State Key Laboratory of Drug Regulatory Science, National Institutes for Food and Drug Control, Beijing, ChinaNational Center for Safety Evaluation of Drugs, State Key Laboratory of Drug Regulatory Science, National Institutes for Food and Drug Control, Beijing, ChinaNational Center for Safety Evaluation of Drugs, State Key Laboratory of Drug Regulatory Science, National Institutes for Food and Drug Control, Beijing, ChinaInstitute for Biological Product Control, National Institutes for Food and Drug Control, Beijing, ChinaNational Center for Safety Evaluation of Drugs, State Key Laboratory of Drug Regulatory Science, National Institutes for Food and Drug Control, Beijing, ChinaNational Center for Safety Evaluation of Drugs, State Key Laboratory of Drug Regulatory Science, National Institutes for Food and Drug Control, Beijing, ChinaObjectiveIn non-clinical safety evaluation of drugs, pathological result is one of the gold standards for determining toxic effects. However, pathological diagnosis might be challenging and affected by pathologist expertise. In carcinogenicity studies, drug-induced squamous cell carcinoma (SCC) of the mouse stomach represents a diagnostic challenge for toxicopathologists. This study aims to establish a detection model for mouse gastric squamous cell carcinoma (GSCC) using deep learning algorithms, to improve the accuracy and consistency of pathological diagnoses.MethodsA total of 93 cases of drug-induced mouse GSCC and 56 cases of normal mouse stomach tissue from carcinogenicity studies were collected. After scanning into digital slides, semi-automated data annotation was performed. All images underwent preprocessing, including tissue extraction, artifact removal, and exclusion of normal epithelial regions. The images were then randomly divided into training, validation, and test sets in an 8:1:1 ratio. Five different convolutional neural networks (CNNs)-FCN, LR-ASPP, DeepLabv3+, U-Net, and DenseNet were applied to identify GSCC and non-GSCC regions. Tumor prediction images (algorithm results shown as overlays) derived from the slide images were compared, and the performance of the constructed models was evaluated using Precision, Recall, and F1-score.ResultsThe Precision, Recall, and F1-scores of DenseNet, U-Net, and DeepLabv3 + algorithms were all above 90%. Specifically, the DenseNet model achieved an overall Precision of 0.9044, Recall of 0.9291, and F1-score of 0.9157 in the test set. Compared to the other algorithms, DenseNet exhibited the highest F1-score and Recall, demonstrating superior generalization ability.ConclusionThe DenseNet algorithm model developed in this study shown promising application potential for assisting in the diagnosis of mouse GSCC. As artificial intelligence (AI) technology continues to advance in non-clinical safety evaluation of drugs, CNN-based toxicological pathology detection models will become essential tools to assist pathologists in precise diagnosis and consistency evaluation.https://www.frontiersin.org/articles/10.3389/fmed.2025.1587417/fullnon-clinical safety evaluation of drugstoxicological pathologyartificial intelligencedeep learninggastric squamous cell carcinoma |
| spellingShingle | Yuke Ren Yuke Ren Shuangxing Li Di Zhang Yongtian Zhao Yanwei Yang Guitao Huo Xiaobing Zhou Xingchao Geng Zhi Lin Zhe Qu Application of deep learning convolutional neural networks to identify gastric squamous cell carcinoma in mice Frontiers in Medicine non-clinical safety evaluation of drugs toxicological pathology artificial intelligence deep learning gastric squamous cell carcinoma |
| title | Application of deep learning convolutional neural networks to identify gastric squamous cell carcinoma in mice |
| title_full | Application of deep learning convolutional neural networks to identify gastric squamous cell carcinoma in mice |
| title_fullStr | Application of deep learning convolutional neural networks to identify gastric squamous cell carcinoma in mice |
| title_full_unstemmed | Application of deep learning convolutional neural networks to identify gastric squamous cell carcinoma in mice |
| title_short | Application of deep learning convolutional neural networks to identify gastric squamous cell carcinoma in mice |
| title_sort | application of deep learning convolutional neural networks to identify gastric squamous cell carcinoma in mice |
| topic | non-clinical safety evaluation of drugs toxicological pathology artificial intelligence deep learning gastric squamous cell carcinoma |
| url | https://www.frontiersin.org/articles/10.3389/fmed.2025.1587417/full |
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