Whole slide image based deep learning refines prognosis and therapeutic response evaluation in lung adenocarcinoma
Abstract Existing prognostic models are useful for estimating the prognosis of lung adenocarcinoma patients, but there remains room for improvement. In the current study, we developed a deep learning model based on histopathological images to predict the recurrence risk of lung adenocarcinoma patien...
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| Main Authors: | , , , , , , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-01-01
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| Series: | npj Digital Medicine |
| Online Access: | https://doi.org/10.1038/s41746-025-01470-z |
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| _version_ | 1832571309264994304 |
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| author | Tao Chen Jialiang Wen Xinchen Shen Jiaqi Shen Jiajun Deng Mengmeng Zhao Long Xu Chunyan Wu Bentong Yu Minglei Yang Minjie Ma Junqi Wu Yunlang She Yifan Zhong Likun Hou Yanrui Jin Chang Chen |
| author_facet | Tao Chen Jialiang Wen Xinchen Shen Jiaqi Shen Jiajun Deng Mengmeng Zhao Long Xu Chunyan Wu Bentong Yu Minglei Yang Minjie Ma Junqi Wu Yunlang She Yifan Zhong Likun Hou Yanrui Jin Chang Chen |
| author_sort | Tao Chen |
| collection | DOAJ |
| description | Abstract Existing prognostic models are useful for estimating the prognosis of lung adenocarcinoma patients, but there remains room for improvement. In the current study, we developed a deep learning model based on histopathological images to predict the recurrence risk of lung adenocarcinoma patients. The efficiency of the model was then evaluated in independent multicenter cohorts. The model defined high- and low-risk groups successfully stratified prognosis of the entire cohort. Moreover, multivariable Cox analysis identified the model defined risk groups as an independent predictor for disease-free survival. Importantly, combining TNM stage with the established model helped to distinguish subgroups of patients with high-risk stage II and stage III disease who are highly likely to benefit from adjuvant chemotherapy. Overall, our study highlights the significant value of the constructed model to serve as a complementary biomarker for survival stratification and adjuvant therapy selection for lung adenocarcinoma patients after resection. |
| format | Article |
| id | doaj-art-74b3c97988674e49b2a1e8264c5198d5 |
| institution | Kabale University |
| issn | 2398-6352 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | npj Digital Medicine |
| spelling | doaj-art-74b3c97988674e49b2a1e8264c5198d52025-02-02T12:43:44ZengNature Portfolionpj Digital Medicine2398-63522025-01-018111210.1038/s41746-025-01470-zWhole slide image based deep learning refines prognosis and therapeutic response evaluation in lung adenocarcinomaTao Chen0Jialiang Wen1Xinchen Shen2Jiaqi Shen3Jiajun Deng4Mengmeng Zhao5Long Xu6Chunyan Wu7Bentong Yu8Minglei Yang9Minjie Ma10Junqi Wu11Yunlang She12Yifan Zhong13Likun Hou14Yanrui Jin15Chang Chen16Department of Thoracic Surgery, Shanghai Pulmonary Hospital, School of Medicine, Tongji UniversityDepartment of Thoracic Surgery, Shanghai Pulmonary Hospital, School of Medicine, Tongji UniversityDepartment of Thoracic Surgery, Shanghai Pulmonary Hospital, School of Medicine, Tongji UniversitySchool of Mathematical Sciences, Shanghai Jiao Tong UniversityDepartment of Thoracic Surgery, Shanghai Pulmonary Hospital, School of Medicine, Tongji UniversityDepartment of Thoracic Surgery, Shanghai Pulmonary Hospital, School of Medicine, Tongji UniversityDepartment of Thoracic Surgery, Shanghai Pulmonary Hospital, School of Medicine, Tongji UniversityDepartment of Pathology, Shanghai Pulmonary Hospital, School of Medicine, Tongji UniversityDepartment of Thoracic Surgery, The First Affiliated Hospital of Nanchang UniversityDepartment of Thoracic Surgery, Ningbo HwaMei Hospital, Chinese Academy of ScienceDepartment of Thoracic Surgery, The First Affiliated Hospital of Lanzhou UniversityDepartment of Thoracic Surgery, Shanghai Pulmonary Hospital, School of Medicine, Tongji UniversityDepartment of Thoracic Surgery, Shanghai Pulmonary Hospital, School of Medicine, Tongji UniversityDepartment of Thoracic Surgery, Shanghai Pulmonary Hospital, School of Medicine, Tongji UniversityDepartment of Pathology, Shanghai Pulmonary Hospital, School of Medicine, Tongji UniversityState Key Laboratory of Mechanical System and Vibration, School of Mechanical Engineering, Shanghai Jiao Tong UniversityDepartment of Thoracic Surgery, Shanghai Pulmonary Hospital, School of Medicine, Tongji UniversityAbstract Existing prognostic models are useful for estimating the prognosis of lung adenocarcinoma patients, but there remains room for improvement. In the current study, we developed a deep learning model based on histopathological images to predict the recurrence risk of lung adenocarcinoma patients. The efficiency of the model was then evaluated in independent multicenter cohorts. The model defined high- and low-risk groups successfully stratified prognosis of the entire cohort. Moreover, multivariable Cox analysis identified the model defined risk groups as an independent predictor for disease-free survival. Importantly, combining TNM stage with the established model helped to distinguish subgroups of patients with high-risk stage II and stage III disease who are highly likely to benefit from adjuvant chemotherapy. Overall, our study highlights the significant value of the constructed model to serve as a complementary biomarker for survival stratification and adjuvant therapy selection for lung adenocarcinoma patients after resection.https://doi.org/10.1038/s41746-025-01470-z |
| spellingShingle | Tao Chen Jialiang Wen Xinchen Shen Jiaqi Shen Jiajun Deng Mengmeng Zhao Long Xu Chunyan Wu Bentong Yu Minglei Yang Minjie Ma Junqi Wu Yunlang She Yifan Zhong Likun Hou Yanrui Jin Chang Chen Whole slide image based deep learning refines prognosis and therapeutic response evaluation in lung adenocarcinoma npj Digital Medicine |
| title | Whole slide image based deep learning refines prognosis and therapeutic response evaluation in lung adenocarcinoma |
| title_full | Whole slide image based deep learning refines prognosis and therapeutic response evaluation in lung adenocarcinoma |
| title_fullStr | Whole slide image based deep learning refines prognosis and therapeutic response evaluation in lung adenocarcinoma |
| title_full_unstemmed | Whole slide image based deep learning refines prognosis and therapeutic response evaluation in lung adenocarcinoma |
| title_short | Whole slide image based deep learning refines prognosis and therapeutic response evaluation in lung adenocarcinoma |
| title_sort | whole slide image based deep learning refines prognosis and therapeutic response evaluation in lung adenocarcinoma |
| url | https://doi.org/10.1038/s41746-025-01470-z |
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