Whole slide image based deep learning refines prognosis and therapeutic response evaluation in lung adenocarcinoma
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.
Authors
- Bentong Yu (ORCID: https://orcid.org/0000-0001-6396-1931)
- Yifan Zhong (ORCID: https://orcid.org/0000-0002-5126-9021)
- Jialiang Wen (ORCID: https://orcid.org/0000-0003-1493-1757)
- Minglei Yang (ORCID: https://orcid.org/0000-0002-6881-337X)
- Minjie Ma (ORCID: https://orcid.org/0000-0002-6297-2097)
- Yanrui Jin (ORCID: https://orcid.org/0000-0001-9489-5447)
- Likun Hou (ORCID: https://orcid.org/0000-0002-1797-8342)
- Xinchen Shen (ORCID: https://orcid.org/0009-0001-7053-1240)
- Jiajun Deng (ORCID: https://orcid.org/0000-0002-6834-0322)
- Tao Chen (ORCID: https://orcid.org/0000-0002-1153-3406)
- Chang Chen (ORCID: https://orcid.org/0000-0002-9981-3110)
- Xu Long
- Junqi Wu (ORCID: https://orcid.org/0000-0001-6560-3281)
- Chunyan Wu (ORCID: https://orcid.org/0000-0001-7119-8139)
- Mengmeng Zhao (ORCID: https://orcid.org/0000-0002-9682-6450)
- Jiaqi Shen (ORCID: https://orcid.org/0000-0002-7435-4106)
- Yunlang She (ORCID: https://orcid.org/0000-0003-2841-1250)
Institutions
- Tongji University (CN)
- Nanchang University (CN)
- Shanghai Jiao Tong University (CN)
- Shanghai Pulmonary Hospital (CN)
- First Hospital of Lanzhou University (CN)
- First Affiliated Hospital of Nanchang University (CN)
- Lanzhou University (CN)
Publication Details
- Journal
- npj Digital Medicine
- Published
- 2025-01-29
- DOI
- https://doi.org/10.1038/s41746-025-01470-z
- Citations
- 8
- Primary Topic
- Lung Cancer Diagnosis and Treatment
- Type
- article
- Field-Weighted Citation Impact
- 12.15
Funders
- Shanghai Municipal Health Commission
- National Natural Science Foundation of China
- Science and Technology Commission of Shanghai Municipality
- Shanghai Pulmonary Hospital
- National Postdoctoral Program for Innovative Talents
- National Key Research and Development Program of China