ArticleScientific reports2019
Pathologist-level classification of histologic patterns on resected lung adenocarcinoma slides with deep neural networks.
Article in Scientific reports, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 155 papers, 1 of them a synthesis that pooled it.
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Who cites it
155 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Transforming histologic assessment: artificial intelligence in cancer diagnosis and personalized treatment.British journal of cancer · 2025Pooled it
- Ensemble learning of pathology foundation models for precision oncology.Cancer cell · 2026Article
- Integration of Histologic Pattern Classifiers and Graph Convolutional Networks for Prognostic Prediction in Lung Adenocarcinoma.JTO clinical and research reports · 2026Article
- A multimodal vision-language model for generalizable annotation-free pathology localization.Nature biomedical engineering · 2026Article
- Applications of Artificial Intelligence in Cancer Diagnosis and Treatment.Cancer medicine · 2026Review
- A benchmark study of vision and pathology foundation models for computational pathology.Nature communications · 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
- Robust Histopathology Subtyping via Perturbation Fidelity in Deep Classifier.Journal of imaging informatics in medicine · 2026Article
- CLWD: a Chinese histopathology dataset for lung adenocarcinoma subtype classification.Scientific data · 2026Article
- Prognostic model for pancreatic cancer based on machine learning of routine slides and transcriptomic tumor analysis.British journal of cancer · 2026Article
- Enhancing colorectal cancer diagnosis with the ferroptosis marker GPX4 and serum biomarkers: A retrospective analysis and machine learning approach.Biochemistry and biophysics reports · 2026Article
- Deep Learning-Based Segmentation of Lung Adenocarcinoma Whole-Slide Images for Objective Grading, Tumor Spread Through Air Spaces Identification, and Mutation Prediction.Modern pathology : an official journal of the United States and Canadian Academy of Pathology, Inc · 2025Article
- Automated classification of lung cancer subtypes cells using microscopic images and ensembled deep learning architectures.Scientific reports · 2025Article
- Deep learning for digital pathology: A critical overview of methodological framework.Journal of pathology informatics · 2025Review
- Self-supervision enhances instance-based multiple instance learning methods in digital pathology: a benchmark study.Journal of medical imaging (Bellingham, Wash.) · 2025Article
- A multimodal whole-slide foundation model for pathology.Nature medicine · 2025Article
- Simultaneous classification and delineation of seven types of histological growth patterns in lung adenocarcinomas using self-supervised learning and online hard patch mining.Quantitative imaging in medicine and surgery · 2025Article
- Predicting targeted therapy resistance in non-small cell lung cancer using multimodal machine learning.Journal of thoracic disease · 2025Article
- Computer Vision-Assisted Spatial Analysis of Mitoses and Vasculature in Lung Cancer.Journal of clinical medicine · 2025Article
- Article
95 more citing papers are in PubMed but not listed here.
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6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Classification of histologic patterns in lung adenocarcinoma is critical for determining tumor grade and treatment for patients. However, this task is often challenging due to the heterogeneous nature of lung adenocarcinoma and the subjective criteria for evaluation. In this study, we propose a deep learning model that automatically classifies the histologic patterns of lung adenocarcinoma on surgical resection slides. Our model uses a convolutional neural network to identify regions of neoplastic cells, then aggregates those classifications to infer predominant and minor histologic patterns for any given whole-slide image. We evaluated our model on an independent set of 143 whole-slide images. It achieved a kappa score of 0.525 and an agreement of 66.6% with three pathologists for classifying the predominant patterns, slightly higher than the inter-pathologist kappa score of 0.485 and agreement of 62.7% on this test set. All evaluation metrics for our model and the three pathologists were within 95% confidence intervals of agreement. If confirmed in clinical practice, our model can assist pathologists in improving classification of lung adenocarcinoma patterns by automatically pre-screening and highlighting cancerous regions prior to review. Our approach can be generalized to any whole-slide image classification task, and code is made publicly available at https://github.com/BMIRDS/deepslide .
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