Optimized digital workflow for pathologist-grade evaluation in bleomycin-induced pulmonary fibrosis mouse model
Abstract Idiopathic pulmonary fibrosis (IPF) is a progressive and ultimately fatal disorder of unknown etiology, characterized by interstitial fibrosis of the lungs. Bleomycin-induced pulmonary fibrosis mouse model (BLM model) is a widely used animal model to evaluate therapeutic targets for IPF. Hi...
Saved in:
| Main Authors: | , , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2025-01-01
|
| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-025-86544-4 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832585830317686784 |
|---|---|
| author | Toshiki Goto Akira Sano Shinichi Onishi Natsuko Hada Rui Kimata Saori Matsuo Sohei Oyama Atsuhiko Kato Hideaki Mizuno Masaki Yamazaki |
| author_facet | Toshiki Goto Akira Sano Shinichi Onishi Natsuko Hada Rui Kimata Saori Matsuo Sohei Oyama Atsuhiko Kato Hideaki Mizuno Masaki Yamazaki |
| author_sort | Toshiki Goto |
| collection | DOAJ |
| description | Abstract Idiopathic pulmonary fibrosis (IPF) is a progressive and ultimately fatal disorder of unknown etiology, characterized by interstitial fibrosis of the lungs. Bleomycin-induced pulmonary fibrosis mouse model (BLM model) is a widely used animal model to evaluate therapeutic targets for IPF. Histopathological analysis of lung fibrosis is an important method for evaluating BLM model. However, this method requires expertise in recognizing complex visual patterns and is time-consuming, making the workflow difficult and inefficient. Therefore, we developed a new workflow for BLM model that reduces inter- and intra-observer variations and improves the evaluation process. We generated deep learning models for grading lung fibrosis that were able to achieve accuracy comparable to that of pathologists. These models incorporate complex image patterns and qualitative factors, such as collagen texture and distribution, potentially identifying drug candidates overlooked in evaluations based solely on simple area extraction. This deep learning-based fibrosis grade assessment has the potential to streamline drug development for pulmonary fibrosis by offering higher granularity and reproducibility in evaluating BLM model. |
| format | Article |
| id | doaj-art-1dbfb8c1ece74bf1be07566bc05f6f3a |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-1dbfb8c1ece74bf1be07566bc05f6f3a2025-01-26T12:26:57ZengNature PortfolioScientific Reports2045-23222025-01-011511710.1038/s41598-025-86544-4Optimized digital workflow for pathologist-grade evaluation in bleomycin-induced pulmonary fibrosis mouse modelToshiki Goto0Akira Sano1Shinichi Onishi2Natsuko Hada3Rui Kimata4Saori Matsuo5Sohei Oyama6Atsuhiko Kato7Hideaki Mizuno8Masaki Yamazaki9Research Division, Chugai Pharmaceutical Co., Ltd.ExaWizards Inc.Translational Research Division, Chugai Pharmaceutical Co., LtdResearch Division, Chugai Pharmaceutical Co., Ltd.ExaWizards Inc.Translational Research Division, Chugai Pharmaceutical Co., LtdResearch Division, Chugai Pharmaceutical Co., Ltd.Translational Research Division, Chugai Pharmaceutical Co., LtdResearch Division, Chugai Pharmaceutical Co., Ltd.Translational Research Division, Chugai Pharmaceutical Co., LtdAbstract Idiopathic pulmonary fibrosis (IPF) is a progressive and ultimately fatal disorder of unknown etiology, characterized by interstitial fibrosis of the lungs. Bleomycin-induced pulmonary fibrosis mouse model (BLM model) is a widely used animal model to evaluate therapeutic targets for IPF. Histopathological analysis of lung fibrosis is an important method for evaluating BLM model. However, this method requires expertise in recognizing complex visual patterns and is time-consuming, making the workflow difficult and inefficient. Therefore, we developed a new workflow for BLM model that reduces inter- and intra-observer variations and improves the evaluation process. We generated deep learning models for grading lung fibrosis that were able to achieve accuracy comparable to that of pathologists. These models incorporate complex image patterns and qualitative factors, such as collagen texture and distribution, potentially identifying drug candidates overlooked in evaluations based solely on simple area extraction. This deep learning-based fibrosis grade assessment has the potential to streamline drug development for pulmonary fibrosis by offering higher granularity and reproducibility in evaluating BLM model.https://doi.org/10.1038/s41598-025-86544-4 |
| spellingShingle | Toshiki Goto Akira Sano Shinichi Onishi Natsuko Hada Rui Kimata Saori Matsuo Sohei Oyama Atsuhiko Kato Hideaki Mizuno Masaki Yamazaki Optimized digital workflow for pathologist-grade evaluation in bleomycin-induced pulmonary fibrosis mouse model Scientific Reports |
| title | Optimized digital workflow for pathologist-grade evaluation in bleomycin-induced pulmonary fibrosis mouse model |
| title_full | Optimized digital workflow for pathologist-grade evaluation in bleomycin-induced pulmonary fibrosis mouse model |
| title_fullStr | Optimized digital workflow for pathologist-grade evaluation in bleomycin-induced pulmonary fibrosis mouse model |
| title_full_unstemmed | Optimized digital workflow for pathologist-grade evaluation in bleomycin-induced pulmonary fibrosis mouse model |
| title_short | Optimized digital workflow for pathologist-grade evaluation in bleomycin-induced pulmonary fibrosis mouse model |
| title_sort | optimized digital workflow for pathologist grade evaluation in bleomycin induced pulmonary fibrosis mouse model |
| url | https://doi.org/10.1038/s41598-025-86544-4 |
| work_keys_str_mv | AT toshikigoto optimizeddigitalworkflowforpathologistgradeevaluationinbleomycininducedpulmonaryfibrosismousemodel AT akirasano optimizeddigitalworkflowforpathologistgradeevaluationinbleomycininducedpulmonaryfibrosismousemodel AT shinichionishi optimizeddigitalworkflowforpathologistgradeevaluationinbleomycininducedpulmonaryfibrosismousemodel AT natsukohada optimizeddigitalworkflowforpathologistgradeevaluationinbleomycininducedpulmonaryfibrosismousemodel AT ruikimata optimizeddigitalworkflowforpathologistgradeevaluationinbleomycininducedpulmonaryfibrosismousemodel AT saorimatsuo optimizeddigitalworkflowforpathologistgradeevaluationinbleomycininducedpulmonaryfibrosismousemodel AT soheioyama optimizeddigitalworkflowforpathologistgradeevaluationinbleomycininducedpulmonaryfibrosismousemodel AT atsuhikokato optimizeddigitalworkflowforpathologistgradeevaluationinbleomycininducedpulmonaryfibrosismousemodel AT hideakimizuno optimizeddigitalworkflowforpathologistgradeevaluationinbleomycininducedpulmonaryfibrosismousemodel AT masakiyamazaki optimizeddigitalworkflowforpathologistgradeevaluationinbleomycininducedpulmonaryfibrosismousemodel |