ArticleJournal of the American Medical Informatics Association : JAMIA2020
Classifying non-small cell lung cancer types and transcriptomic subtypes using convolutional neural networks.
Article in Journal of the American Medical Informatics Association : JAMIA, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 61 papers.
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61 citing papers in PubMed.
- Label-free pathological subtyping of non-small cell lung cancer using deep classification and virtual immunohistochemical staining.NPJ digital medicine · 2026Article
- Artificial intelligence-enabled histological analysis in pre-clinical respiratory disease models: a scoping review.European respiratory review : an official journal of the European Respiratory Society · 2026Article
- Pathological lung tissue changes in common infectious diseases.Bioinformation · 2026Article
- Separability of histopathology foundation model embeddings of stimulated Raman histology images for lung carcinoma subtyping.Journal of Cancer · 2026Article
- Contrastive learning enhances fairness in pathology artificial intelligence systems.Cell reports. Medicine · 2025Article
- Automated classification of lung cancer subtypes cells using microscopic images and ensembled deep learning architectures.Scientific reports · 2025Article
- Analyzing histopathological images using fused CNN features based on the geometric active contour method for early diagnosis of lung and colon cancer.Discover oncology · 2025Article
- Deep learning-based fusion of nuclear segmentation features for microsatellite instability and tumor mutational burden prediction in digestive tract cancers: a multicenter validation study.Briefings in bioinformatics · 2025Article
- Unraveling the molecular-pathological characteristics and cellular complexity of the tumor immune microenvironment in metastatic non-small cell lung cancer.Cell communication and signaling : CCS · 2025Review
- Deep learning enhances precision diagnosis and treatment of non-small cell lung cancer: future prospects.Translational lung cancer research · 2025Review
- Enhanced prediction of breast cancer patient response to chemotherapy by integrating deconvolved expression patterns of immune, stromal and tumor cells.bioRxiv : the preprint server for biology · 2025Article
- Systematic scoping review of external validation studies of AI pathology models for lung cancer diagnosis.NPJ precision oncology · 2025Article
- Integration of histopathological image features and multi-dimensional omics data in predicting molecular features and survival in glioblastoma.Frontiers in medicine · 2025Article
- Role of Nanoformulations in the Treatment of Lung Cancer.Recent patents on nanotechnology · 2025Review
- Performance of externally validated machine learning models based on histopathology images for the diagnosis, classification, prognosis, or treatment outcome prediction in female breast cancer: A systematic review.Journal of pathology informatics · 2024Review
- Article
- A deep-learning framework to predict cancer treatment response from histopathology images through imputed transcriptomics.Nature cancer · 2024Article
- Advancing Precision Medicine: Algebraic Topology and Differential Geometry in Radiology and Computational Pathology.Laboratory investigation; a journal of technical methods and pathology · 2024Review
- Artificial intelligence in digital pathology: a systematic review and meta-analysis of diagnostic test accuracy.NPJ digital medicine · 2024Review
- Deep learning of cell spatial organizations identifies clinically relevant insights in tissue images.Nature communications · 2023Article
1 more citing papers are in PubMed but not listed here.
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7 authors.
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Abstract
objectiveNon-small cell lung cancer is a leading cause of cancer death worldwide, and histopathological evaluation plays the primary role in its diagnosis. However, the morphological patterns associated with the molecular subtypes have not been systematically studied. To bridge this gap, we developed a quantitative histopathology analytic framework to identify the types and gene expression subtypes of non-small cell lung cancer objectively. MATERIALS AND
methodsWe processed whole-slide histopathology images of lung adenocarcinoma (n = 427) and lung squamous cell carcinoma patients (n = 457) in the Cancer Genome Atlas. We built convolutional neural networks to classify histopathology images, evaluated their performance by the areas under the receiver-operating characteristic curves (AUCs), and validated the results in an independent cohort (n = 125).
resultsTo establish neural networks for quantitative image analyses, we first built convolutional neural network models to identify tumor regions from adjacent dense benign tissues (AUCs > 0.935) and recapitulated expert pathologists' diagnosis (AUCs > 0.877), with the results validated in an independent cohort (AUCs = 0.726-0.864). We further demonstrated that quantitative histopathology morphology features identified the major transcriptomic subtypes of both adenocarcinoma and squamous cell carcinoma (P < .01). DISCUSSION: Our study is the first to classify the transcriptomic subtypes of non-small cell lung cancer using fully automated machine learning methods. Our approach does not rely on prior pathology knowledge and can discover novel clinically relevant histopathology patterns objectively. The developed procedure is generalizable to other tumor types or diseases.
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