ArticleCancers2021
DeepRePath: Identifying the Prognostic Features of Early-Stage Lung Adenocarcinoma Using Multi-Scale Pathology Images and Deep Convolutional Neural Networks.
Article in Cancers, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers, 1 of them a synthesis that pooled it.
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Who cites it
20 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Tumor Budding as a Marker for Poor Prognosis and Epithelial-Mesenchymal Transition in Lung Cancer: A Systematic Review and Meta-Analysis.Frontiers in oncology · 2022Pooled it
- hsa-let-7b-5p-associated BUB1/TMPO-AS1 ceRNA axis identified as a potential biomarker in lung adenocarcinoma.Cell division · 2025Article
- Deep learning enhances precision diagnosis and treatment of non-small cell lung cancer: future prospects.Translational lung cancer research · 2025Review
- Predicting the prognosis of radical gastrectomy for patients with locally advanced gastric cancer after neoadjuvant chemotherapy using machine learning technology: a multicenter study in China.Surgical endoscopy · 2025Article
- Accurate prediction of disease-free and overall survival in non-small cell lung cancer using patient-level multimodal weakly supervised learning.NPJ precision oncology · 2025Article
- Deep Learning Analysis for Predicting Tumor Spread through Air Space in Early-Stage Lung Adenocarcinoma Pathology Images.Cancers · 2024Article
- A new model using deep learning to predict recurrence after surgical resection of lung adenocarcinoma.Scientific reports · 2024Article
- Review
- Predicting Non-Small-Cell Lung Cancer Survival after Curative Surgery via Deep Learning of Diffusion MRI.Diagnostics (Basel, Switzerland) · 2023Article
- Recent Advancements in Deep Learning Using Whole Slide Imaging for Cancer Prognosis.Bioengineering (Basel, Switzerland) · 2023Review
- Histopathological Analysis for Detecting Lung and Colon Cancer Malignancies Using Hybrid Systems with Fused Features.Bioengineering (Basel, Switzerland) · 2023Article
- Deep learning facilitates multi-data type analysis and predictive biomarker discovery in cancer precision medicine.Computational and structural biotechnology journal · 2023Review
- Genetic predisposition to lung adenocarcinoma outcome is a feature already present in patients' noninvolved lung tissue.Cancer science · 2023Article
- Multi-scale pathology image texture signature is a prognostic factor for resectable lung adenocarcinoma: a multi-center, retrospective study.Journal of translational medicine · 2022Article
- Multi-modality artificial intelligence in digital pathology.Briefings in bioinformatics · 2022Review
- Application of Artificial Intelligence in Pathology: Trends and Challenges.Diagnostics (Basel, Switzerland) · 2022Review
- Deep learning-based prediction of molecular cancer biomarkers from tissue slides: A new tool for precision oncology.Clinical and molecular hepatology · 2022Review
- Machine Learning-Based Model for the Prognosis of Postoperative Gastric Cancer.Cancer management and research · 2022Article
- Comprehensive Analysis and Reinforcement Learning of Hypoxic Genes Based on Four Machine Learning Algorithms for Estimating the Immune Landscape, Clinical Outcomes, and Therapeutic Implications in Patients With Lung Adenocarcinoma.Frontiers in immunology · 2022Article
- Deep learning can predict survival directly from histology in clear cell renal cell carcinoma.PloS one · 2022Article
Corrections and comments
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Authors and funding
16 authors.
Funding
Abstract
The prognosis of patients with lung adenocarcinoma (LUAD), especially early-stage LUAD, is dependent on clinicopathological features. However, its predictive utility is limited. In this study, we developed and trained a DeepRePath model based on a deep convolutional neural network (CNN) using multi-scale pathology images to predict the prognosis of patients with early-stage LUAD. DeepRePath was pre-trained with 1067 hematoxylin and eosin-stained whole-slide images of LUAD from the Cancer Genome Atlas. DeepRePath was further trained and validated using two separate CNNs and multi-scale pathology images of 393 resected lung cancer specimens from patients with stage I and II LUAD. Of the 393 patients, 95 patients developed recurrence after surgical resection. The DeepRePath model showed average area under the curve (AUC) scores of 0.77 and 0.76 in cohort I and cohort II (external validation set), respectively. Owing to low performance, DeepRePath cannot be used as an automated tool in a clinical setting. When gradient-weighted class activation mapping was used, DeepRePath indicated the association between atypical nuclei, discohesive tumor cells, and tumor necrosis in pathology images showing recurrence. Despite the limitations associated with a relatively small number of patients, the DeepRePath model based on CNNs with transfer learning could predict recurrence after the curative resection of early-stage LUAD using multi-scale pathology images.
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