ArticleEBioMedicine2019
ConvPath: A software tool for lung adenocarcinoma digital pathological image analysis aided by a convolutional neural network.
Article in EBioMedicine, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 62 papers, 2 of them syntheses that pooled it.
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Who cites it
62 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Deep learning for detecting early gastric cancer with white-light endoscopy: a systematic review and meta-analysis.Frontiers in artificial intelligence · 2026Pooled it
- The value of artificial intelligence in the diagnosis of lung cancer: A systematic review and meta-analysis.PloS one · 2023Pooled it
- Automatic ganglion cell detection for improving the efficiency and accuracy of hirschprung disease diagnosis.Scientific reports · 2021Trial
- Scalable 3D cell-interaction analysis via supercell graphs for prostate cancer risk stratification.bioRxiv : the preprint server for biology · 2026Article
- Current trends and future directions of artificial intelligence in lung cancer diagnosis.Chinese journal of cancer research = Chung-kuo yen cheng yen chiu · 2026Article
- Deep learning-driven recognition of panoramic tumor microenvironment features in H&E sections and its application.Journal for immunotherapy of cancer · 2026Review
- Estimating major pathological response in non-small cell lung cancer patients with post-neoadjuvant therapy using MMT-net.Scientific reports · 2026Article
- Integrative Genomic and AI Approaches to Lung Cancer and Implications for Disease Prevention in Former Smokers.International journal of molecular sciences · 2026Review
- Automated detection of mycobacterium tuberculosis based on cloud computing.BMC infectious diseases · 2025Article
- Artificial intelligence in cancer: applications, challenges, and future perspectives.Molecular cancer · 2025Review
- Leveraging deep learning to discover interpretable cellular spatial biomarkers for prognostic predictions based on hepatocellular carcinoma histology.The journal of pathology. Clinical research · 2025Article
- Systematic scoping review of external validation studies of AI pathology models for lung cancer diagnosis.NPJ precision oncology · 2025Article
- A review of enhanced biosignature immunotherapy tools for predicting lung cancer immune phenotypes using deep learning.Discover oncology · 2025Review
- Enhanced Superpixel-Guided ResNet Framework with Optimized Deep-Weighted Averaging-Based Feature Fusion for Lung Cancer Detection in Histopathological Images.Diagnostics (Basel, Switzerland) · 2025Article
- Computational pathology: A survey review and the way forward.Journal of pathology informatics · 2024Review
- Artificial intelligence applications in personalizing lung cancer management: state of the art and future perspectives.Journal of thoracic disease · 2024Review
- Review
- Combined expert-in-the-loop-random forest multiclass segmentation U-net based artificial intelligence model: evaluation of non-small cell lung cancer in fibrotic and non-fibrotic microenvironments.Journal of translational medicine · 2024Article
- Information-incorporated sparse hierarchical cancer heterogeneity analysis.Statistics in medicine · 2024Article
- A new model using deep learning to predict recurrence after surgical resection of lung adenocarcinoma.Scientific reports · 2024Article
2 more citing papers are in PubMed but not listed here.
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Authors and funding
18 authors.
Funding
Abstract
backgroundThe spatial distributions of different types of cells could reveal a cancer cell's growth pattern, its relationships with the tumor microenvironment and the immune response of the body, all of which represent key "hallmarks of cancer". However, the process by which pathologists manually recognize and localize all the cells in pathology slides is extremely labor intensive and error prone.
methodsIn this study, we developed an automated cell type classification pipeline, ConvPath, which includes nuclei segmentation, convolutional neural network-based tumor cell, stromal cell, and lymphocyte classification, and extraction of tumor microenvironment-related features for lung cancer pathology images. To facilitate users in leveraging this pipeline for their research, all source scripts for ConvPath software are available at https://qbrc.swmed.edu/projects/cnn/.
findingsThe overall classification accuracy was 92.9% and 90.1% in training and independent testing datasets, respectively. By identifying cells and classifying cell types, this pipeline can convert a pathology image into a "spatial map" of tumor, stromal and lymphocyte cells. From this spatial map, we can extract features that characterize the tumor micro-environment. Based on these features, we developed an image feature-based prognostic model and validated the model in two independent cohorts. The predicted risk group serves as an independent prognostic factor, after adjusting for clinical variables that include age, gender, smoking status, and stage.
interpretationThe analysis pipeline developed in this study could convert the pathology image into a "spatial map" of tumor cells, stromal cells and lymphocytes. This could greatly facilitate and empower comprehensive analysis of the spatial organization of cells, as well as their roles in tumor progression and metastasis.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.